{
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  "metadata": {
    "colab": {
      "name": "05_Logistic_Regression",
      "provenance": [],
      "collapsed_sections": [
        "CYH7kRTd9WoJ"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsUcHSE0TcRM",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://practicalai.me\"><img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/rounded_logo.png\" width=\"100\" align=\"left\" hspace=\"20px\" vspace=\"20px\"></a>\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/05_Logistic_Regression/logistic_regression.png\" width=\"170\" vspace=\"10px\" align=\"right\">\n",
        "\n",
        "<div align=\"left\">\n",
        "<h1>Logistic Regression</h1>\n",
        "\n",
        "In this lesson, we're going to implement logistic regression for a classification task where we want to probabilistically determine the outcome for a given set of inputs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-CQd9joTxwB",
        "colab_type": "text"
      },
      "source": [
        "<table align=\"center\">\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/rounded_logo.png\" width=\"25\"><a target=\"_blank\" href=\"https://practicalai.me\"> View on practicalAI</a>\n",
        "  </td>\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/colab_logo.png\" width=\"25\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/practicalAI/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb\"> Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/github_logo.png\" width=\"22\"><a target=\"_blank\" href=\"https://github.com/practicalAI/practicalAI/blob/master/notebooks/05_Logistic_Regression.ipynb\"> View code on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VoMq0eFRvugb",
        "colab_type": "text"
      },
      "source": [
        "# Overview"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4Y55tpzIjOa",
        "colab_type": "text"
      },
      "source": [
        "* **Objective:**  Predict the probability of class $y$ given the inputs $X$. The softmax classifier normalizes the linear outputs to determine class probabilities. \n",
        "* **Advantages:**\n",
        "  * Can predict class probabilities given a set on inputs.\n",
        "* **Disadvantages:**\n",
        "  * Sensitive to outliers since objective is to minimize cross entropy loss. Support vector machines ([SVMs](https://towardsdatascience.com/support-vector-machine-vs-logistic-regression-94cc2975433f)) are a good alternative to counter outliers.\n",
        "* **Miscellaneous:** Softmax classifier is going to used widely in neural network architectures as the last layer since it produces class probabilities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "orlWnRz2-wyO"
      },
      "source": [
        "Logistic regression is just an extension on linear regression (both are generalized linear methods). We will still learn to model a line (plane) that models $y$ given $X$. Except now we are dealing with classification problems as opposed to regression problems so we want classification probabilities. We'll be using the softmax operation to normalize our logits ($XW$) to derive probabilities.\n",
        "\n",
        "Our goal is to learn a logistic model $\\hat{y}$ that models $y$ given $X$. \n",
        "\n",
        "$ \\hat{y} = \\frac{e^{XW_y}}{\\sum_j e^{XW}} $ \n",
        "* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NX1}$ ($N$ is the number of samples)\n",
        "* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n",
        "* $W$ = weights | $\\in \\mathbb{R}^{DXC}$ ($C$ is the number of classes)\n",
        "\n",
        "This function is known as the multinomial logistic regression or the softmax classifier. The softmax classifier will use the linear equation ($z=XW$) and normalize it (using the softmax function) to produce the probabiltiy for class y given the inputs.\n",
        "\n",
        "**NOTE**: We'll leave the bias weights out for now to avoid complicating the backpropagation calculation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jq65LZJbSpzd",
        "colab_type": "text"
      },
      "source": [
        "### Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HBPn8zPTQfZ",
        "colab_type": "text"
      },
      "source": [
        "1. Randomly initialize the model's weights $W$ (we'll cover more effective initalization strategies in future lessons).\n",
        "2. Feed inputs $X$ into the model to receive the logits ($z=XW$). Apply the softmax operation on the logits to get the class probabilies $\\hat{y}$ in one-hot encoded form. For example, if there are three classes, the predicted class probabilities could look like [0.3, 0.3, 0.4]. \n",
        "  * $ \\hat{y} = softmax(z) = softmax(XW) = \\frac{e^{XW_y}}{\\sum_j e^{XW}} $\n",
        "3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for logistics regression is cross-entropy loss. \n",
        "  * $J(\\theta) = - \\sum_i y_i ln (\\hat{y_i}) =  - \\sum_i y_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $\n",
        "    * example: $y$ = [0, 0, 1], $\\hat{y}$ = [0.3, 0.3, 0.4]\n",
        "    * $J(\\theta) = - \\sum_i y_i ln (\\hat{y_i}) =  - \\sum_i y_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) = - \\sum_i [0 * ln(0.3) + 0 * ln(0.3) + 1 * ln(0.4)] = -ln(0.4) $\n",
        "  * This simplifies our cross entropy objective to the following: $J(\\theta) = - ln(\\hat{y_i})$ (negative log likelihood). This is only applicable when each input maps to exactly one class.\n",
        "    * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $\n",
        "4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. Let's assume that our classes are mutually exclusive (a set of inputs could only belong to one class).\n",
        " * $\\frac{\\partial{J}}{\\partial{W_j}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}0 - e^{XW_y}e^{XW_j}X}{(\\sum_j e^{XW})^2} = \\frac{Xe^{XW_j}}{\\sum_j e^{XW}} = X\\hat{y}$\n",
        "  * $\\frac{\\partial{J}}{\\partial{W_y}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}X - e^{W_yX}e^{XW_y}X}{(\\sum_j e^{XW})^2} = \\frac{1}{\\hat{y}}(X\\hat{y} - X\\hat{y}^2) = X(\\hat{y}-1)$\n",
        "5. Update the weights $W$ using a small learning rate $\\alpha$. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n",
        "  * $W_j = W_j - \\alpha\\frac{\\partial{J}}{\\partial{W_j}}$\n",
        "6. Repeat steps 2 - 5 to minimize the loss and train the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_hKrjzdtTgM",
        "colab_type": "text"
      },
      "source": [
        "# Set up"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F3X_qTOKSmn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4b5c47fb-6408-43cc-de4b-62af139e289f"
      },
      "source": [
        "# Use TensorFlow 2.x\n",
        "%tensorflow_version 2.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H385V4VUtWOv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-drUMY9CFlQu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Arguments\n",
        "SEED = 1234\n",
        "DATA_FILE = 'tumors.csv'\n",
        "TRAIN_SIZE = 0.7\n",
        "VAL_SIZE = 0.15\n",
        "TEST_SIZE = 0.15\n",
        "NUM_EPOCHS = 100\n",
        "BATCH_SIZE = 32\n",
        "LEARNING_RATE = 1e-1\n",
        "INPUT_DIM = 2\n",
        "HIDDEN_DIM = 1\n",
        "NUM_CLASSES = 2\n",
        "SHUFFLE = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pL67TlZO6Zg4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set seed for reproducability\n",
        "np.random.seed(SEED)\n",
        "tf.random.set_seed(SEED)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00B5uEM8Tb0d",
        "colab_type": "text"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYNIM3PxQGgo",
        "colab_type": "text"
      },
      "source": [
        "We'll used some synthesized data to train our models on. The task is to determine whether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. Note that this is a synethic dataset that has no clinical relevance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8rspXhNEgrL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from pandas.plotting import scatter_matrix\n",
        "import urllib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyCEgQ5NCumb",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sp_tSyItf1_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Upload data from GitHub to notebook's local drive\n",
        "url = \"https://raw.githubusercontent.com/practicalAI/practicalAI/master/data/tumors.csv\"\n",
        "response = urllib.request.urlopen(url)\n",
        "html = response.read()\n",
        "with open(DATA_FILE, 'wb') as fp:\n",
        "    fp.write(html)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7alqmyzXtgE8",
        "colab_type": "code",
        "outputId": "3d1a31c8-350e-48a8-efd8-7e1828424fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Read from CSV to Pandas DataFrame\n",
        "df = pd.read_csv(DATA_FILE, header=0)\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "      <th>tumor_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13.472969</td>\n",
              "      <td>15.250393</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.805510</td>\n",
              "      <td>14.109676</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13.834053</td>\n",
              "      <td>15.793920</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.572811</td>\n",
              "      <td>17.873286</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.633667</td>\n",
              "      <td>16.598559</td>\n",
              "      <td>malignant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure tumor_class\n",
              "0        13.472969       15.250393   malignant\n",
              "1        10.805510       14.109676   malignant\n",
              "2        13.834053       15.793920   malignant\n",
              "3         9.572811       17.873286   malignant\n",
              "4         7.633667       16.598559   malignant"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aqJxfKmnYTs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define X and y\n",
        "X = df[['leukocyte_count', 'blood_pressure']].values\n",
        "y = df['tumor_class'].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpoF3_EgDtPl",
        "colab_type": "code",
        "outputId": "d06c71f3-f8d7-43f4-cccd-3d7e632f48d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Plot data\n",
        "colors = {'benign': 'red', 'malignant': 'blue'}\n",
        "plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n",
        "plt.xlabel('leukocyte count')\n",
        "plt.ylabel('blood pressure')\n",
        "plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OyEmt9jO2b9+VI0aMoFbbM43SFoQB7Nu3L5s0aUuNZrDDufW0WFyp0YgE9jAl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\nnvidcy7DeOJ3zrkM44nfOecyzP8BfwdZPZ1K+7MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "23rUN1uTpoc4",
        "colab_type": "text"
      },
      "source": [
        "# Split data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHcoL7fH_CsS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import collections\n",
        "import json\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0YPFskY1j1T",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Mak_supAsIV",
        "colab_type": "text"
      },
      "source": [
        "Splitting the dataset for classification tasks requires more than just randomly splitting into train, validation and test sets. We want to ensure that each split has a similar class distribution so that we can learn to predict well across all the classes. We can do this by specifying the `stratify` argument in [`train_test_split`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) which will be set to the class labels."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27h91LX0pp7F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_val_test_split(X, y, val_size, test_size, shuffle):\n",
        "    \"\"\"Split data into train/val/test datasets.\n",
        "    \"\"\"\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size=test_size, stratify=y, shuffle=shuffle)\n",
        "    X_train, X_val, y_train, y_val = train_test_split(\n",
        "        X_train, y_train, test_size=val_size, stratify=y_train, shuffle=shuffle)\n",
        "    return X_train, X_val, X_test, y_train, y_val, y_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iH6bWZ221l6J",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4fy2vUjr-Qe",
        "colab_type": "code",
        "outputId": "4968fe38-8cdd-4ca9-beed-f96072f626d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# Create data splits\n",
        "X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n",
        "    X=X, y=y, val_size=VAL_SIZE, test_size=TEST_SIZE, shuffle=SHUFFLE)\n",
        "class_counts = dict(collections.Counter(y))\n",
        "print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n",
        "print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n",
        "print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n",
        "print (f\"X_train[0]: {X_train[0]}\")\n",
        "print (f\"y_train[0]: {y_train[0]}\")\n",
        "print (f\"Classes: {class_counts}\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train: (722, 2), y_train: (722,)\n",
            "X_val: (128, 2), y_val: (128,)\n",
            "X_test: (150, 2), y_test: (150,)\n",
            "X_train[0]: [18.01865938 15.48133647]\n",
            "y_train[0]: benign\n",
            "Classes: {'malignant': 611, 'benign': 389}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLHG5_vpHQ3d",
        "colab_type": "text"
      },
      "source": [
        "# Label encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGWO8zDCHTFp",
        "colab_type": "text"
      },
      "source": [
        "You'll notice that our class labels are text. We need to encode them into integers so we can use them in our models. We're going to the scikit-learn's [LabelEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) to do this. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPIsL1kAHkhu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcfHtYFtHkmo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Output vectorizer\n",
        "y_tokenizer = LabelEncoder()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hbPtvg4HoqE",
        "colab_type": "code",
        "outputId": "c83870d1-c378-481f-9641-ef9a814f78c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Fit on train data\n",
        "y_tokenizer = y_tokenizer.fit(y_train)\n",
        "classes = y_tokenizer.classes_\n",
        "print (f\"classes: {classes}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classes: ['benign' 'malignant']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7MTxcrcHotG",
        "colab_type": "code",
        "outputId": "6504038a-6706-4a90-c95b-2da6b8c58c4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Convert labels to tokens\n",
        "print (f\"y_train[0]: {y_train[0]}\")\n",
        "y_train = y_tokenizer.transform(y_train)\n",
        "y_val = y_tokenizer.transform(y_val)\n",
        "y_test = y_tokenizer.transform(y_test)\n",
        "print (f\"y_train[0]: {y_train[0]}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_train[0]: benign\n",
            "y_train[0]: 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXHboWkUAIjI",
        "colab_type": "code",
        "outputId": "09f872ec-af58-400a-ca27-86fe1c0d20e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Class weights\n",
        "counts = collections.Counter(y_train)\n",
        "class_weights = {_class: 1.0/count for _class, count in counts.items()}\n",
        "print (f\"class counts: {counts},\\nclass weights: {class_weights}\")"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class counts: Counter({1: 441, 0: 281}),\n",
            "class weights: {0: 0.0035587188612099642, 1: 0.0022675736961451248}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkAmzEwVHs6E",
        "colab_type": "text"
      },
      "source": [
        "<img height=\"45\" src=\"http://bestanimations.com/HomeOffice/Lights/Bulbs/animated-light-bulb-gif-29.gif\" align=\"left\" vspace=\"5px\" hspace=\"10px\">\n",
        "\n",
        "Class weights are useful for weighting the loss function during training. It tells the model to focus on samples from an under-represented class."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IodMyhnAJUg_",
        "colab_type": "text"
      },
      "source": [
        "# Standardize data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRhNHi9YJfDm",
        "colab_type": "text"
      },
      "source": [
        "We need to standardize our data (zero mean and unit variance) in order to optimize quickly. We're only going to standardize the inputs X because out outputs y are class values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjIz1KKYJWI7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hP6VTXSiJWM1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Standardize the data (mean=0, std=1) using training data\n",
        "X_scaler = StandardScaler().fit(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kn2SDd28Jwna",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Apply scaler on training and test data (don't standardize outputs for classification)\n",
        "standardized_X_train = X_scaler.transform(X_train)\n",
        "standardized_X_val = X_scaler.transform(X_val)\n",
        "standardized_X_test = X_scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zBAu7GkJJwq8",
        "colab_type": "code",
        "outputId": "fa304558-a464-4bb5-a8b4-b9b4af51a62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Check\n",
        "print (f\"standardized_X_train: mean: {np.mean(standardized_X_train, axis=0)[0]}, std: {np.std(standardized_X_train, axis=0)[0]}\")\n",
        "print (f\"standardized_X_val: mean: {np.mean(standardized_X_val, axis=0)[0]}, std: {np.std(standardized_X_val, axis=0)[0]}\")\n",
        "print (f\"standardized_X_test: mean: {np.mean(standardized_X_test, axis=0)[0]}, std: {np.std(standardized_X_test, axis=0)[0]}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "standardized_X_train: mean: 3.938600753633857e-15, std: 0.9999999999999998\n",
            "standardized_X_val: mean: 0.06571155649025341, std: 0.9625041074006321\n",
            "standardized_X_test: mean: -0.09679265967370689, std: 0.9864056087200104\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYH7kRTd9WoJ",
        "colab_type": "text"
      },
      "source": [
        "# From scratch\n",
        "\n",
        "Before we use TensorFlow 2.0 + Keras we will implent logistic regression from scratch using NumPy so we can:\n",
        "1. Absorb the fundamental concepts by implementing from scratch\n",
        "2. Appreciate the level of abstraction TensorFlow provides\n",
        "\n",
        "<div align=\"left\">\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/lightbulb.gif\" width=\"45px\" align=\"left\" hspace=\"10px\">\n",
        "</div>\n",
        "\n",
        "It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using TensorFlow + Keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7kQ_lBj9c7t",
        "colab_type": "code",
        "outputId": "7caa3354-7fb7-43ba-8fd1-f7c6e2984172",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print (f\"X: {standardized_X_train.shape}\")\n",
        "print (f\"y: {y_train.shape}\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X: (722, 2)\n",
            "y: (722,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-gUbD71-yiX",
        "colab_type": "text"
      },
      "source": [
        "Our goal is to learn a logistic model $\\hat{y}$ that models $y$ given $X$. \n",
        "\n",
        "$ \\hat{y} = \\frac{e^{XW_y}}{\\sum e^{XW}} $ \n",
        "* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NX1}$ ($N$ is the number of samples)\n",
        "* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n",
        "* $W$ = weights | $\\in \\mathbb{R}^{DXC}$ ($C$ is the number of classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wcc5lbdH_Hi",
        "colab_type": "text"
      },
      "source": [
        "We are going to use multinomial logistic regression even though our task only involves two classes because you can generalize the softmax classifier to any number of classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-Os5f8mad5_",
        "colab_type": "text"
      },
      "source": [
        "1. Randomly initialize the model's weights $W$."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6x4heep29c_F",
        "colab_type": "code",
        "outputId": "09de20d2-3ab9-4db5-ced8-2d4e271021ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Initialize random weights\n",
        "W = 0.01 * np.random.randn(INPUT_DIM, NUM_CLASSES)\n",
        "b = np.zeros((1, NUM_CLASSES))\n",
        "print (f\"W: {W.shape}\")\n",
        "print (f\"b: {b.shape}\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W: (2, 2)\n",
            "b: (1, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Fh8YOJjae28",
        "colab_type": "text"
      },
      "source": [
        "2. Feed inputs $X$ into the model to receive the logits ($z=XW$). Apply the softmax operation on the logits to get the class probabilies $\\hat{y}$ in one-hot encoded form. For example, if there are three classes, the predicted class probabilities could look like [0.3, 0.3, 0.4]. \n",
        "  * $ \\hat{y} = softmax(z) = softmax(XW) = \\frac{e^{XW_y}}{\\sum_j e^{XW}} $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXQkZOwW-otw",
        "colab_type": "code",
        "outputId": "3e5af5ef-15fc-4bbd-d052-2609c9491369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Forward pass [NX2] · [2X2] + [1,2] = [NX2]\n",
        "logits = np.dot(standardized_X_train, W) + b\n",
        "print (f\"logits: {logits.shape}\")\n",
        "print (f\"sample: {logits[0]}\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "logits: (722, 2)\n",
            "sample: [0.01817675 0.00635562]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ak9FfYIJ-ozJ",
        "colab_type": "code",
        "outputId": "9613108f-ea18-4480-be40-fc416548ccd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Normalization via softmax to obtain class probabilities\n",
        "exp_logits = np.exp(logits)\n",
        "y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "print (f\"y_hat: {y_hat.shape}\")\n",
        "print (f\"sample: {y_hat[0]}\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y_hat: (722, 2)\n",
            "sample: [0.50295525 0.49704475]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxxORg12amXv",
        "colab_type": "text"
      },
      "source": [
        " 3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for logistics regression is cross-entropy loss. \n",
        "\n",
        "  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dfuMmW2Y01f",
        "colab_type": "code",
        "outputId": "54ac6dbb-a362-4b8e-a181-24ebc5c39046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Loss\n",
        "correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n",
        "loss = np.sum(correct_class_logprobs) / len(y_train)\n",
        "print (f\"loss: {loss:.2f}\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss: 0.69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9nWUa-3b2lw",
        "colab_type": "text"
      },
      "source": [
        "4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. Let's assume that our classes are mutually exclusive (a set of inputs could only belong to one class).\n",
        " * $\\frac{\\partial{J}}{\\partial{W_j}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}0 - e^{XW_y}e^{XW_j}X}{(\\sum_j e^{XW})^2} = \\frac{Xe^{XW_j}}{\\sum_j e^{XW}} = X\\hat{y}$\n",
        "  * $\\frac{\\partial{J}}{\\partial{W_y}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}X - e^{W_yX}e^{XW_y}X}{(\\sum_j e^{XW})^2} = \\frac{1}{\\hat{y}}(X\\hat{y} - X\\hat{y}^2) = X(\\hat{y}-1)$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_-LGJWwZqhW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Backpropagation\n",
        "dscores = y_hat\n",
        "dscores[range(len(y_hat)), y_train] -= 1\n",
        "dscores /= len(y_train)\n",
        "dW = np.dot(standardized_X_train.T, dscores)\n",
        "db = np.sum(dscores, axis=0, keepdims=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaOhLrqib7t9",
        "colab_type": "text"
      },
      "source": [
        "5. Update the weights  𝑊  using a small learning rate  𝛼. The updates will penalize the probabiltiy for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n",
        "  * $W_j = W_j - \\alpha\\frac{\\partial{J}}{\\partial{W_j}}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy6LjHNPZqe1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Update weights\n",
        "W += -LEARNING_RATE * dW\n",
        "b += -LEARNING_RATE * db"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFjnVvCqb_Kk",
        "colab_type": "text"
      },
      "source": [
        "6. Repeat steps 2 - 5 to minimize the loss and train the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vCipe3EhZqbl",
        "colab_type": "code",
        "outputId": "c68d130c-3862-4427-f14c-6572a7aee700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Initialize random weights\n",
        "W = 0.01 * np.random.randn(INPUT_DIM, NUM_CLASSES)\n",
        "b = np.zeros((1, NUM_CLASSES))\n",
        "\n",
        "# Training loop\n",
        "for epoch_num in range(NUM_EPOCHS):\n",
        "\n",
        "    # Forward pass [NX2] · [2X2] = [NX2]\n",
        "    logits = np.dot(standardized_X_train, W) + b\n",
        "    \n",
        "    # Normalization via softmax to obtain class probabilities\n",
        "    exp_logits = np.exp(logits)\n",
        "    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "\n",
        "    # Loss\n",
        "    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n",
        "    loss = np.sum(correct_class_logprobs) / len(y_train)\n",
        "\n",
        "    # show progress\n",
        "    if epoch_num%10 == 0:\n",
        "        # Accuracy\n",
        "        y_pred = np.argmax(logits, axis=1)\n",
        "        accuracy =  np.mean(np.equal(y_train, y_pred))\n",
        "        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n",
        "\n",
        "    # Backpropagation\n",
        "    dscores = y_hat\n",
        "    dscores[range(len(y_hat)), y_train] -= 1\n",
        "    dscores /= len(y_train)\n",
        "    dW = np.dot(standardized_X_train.T, dscores)\n",
        "    db = np.sum(dscores, axis=0, keepdims=True)\n",
        "\n",
        "    # Update weights\n",
        "    W += -LEARNING_RATE * dW\n",
        "    b += -LEARNING_RATE * db"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, loss: 0.684, accuracy: 0.889\n",
            "Epoch: 10, loss: 0.447, accuracy: 0.978\n",
            "Epoch: 20, loss: 0.348, accuracy: 0.978\n",
            "Epoch: 30, loss: 0.295, accuracy: 0.981\n",
            "Epoch: 40, loss: 0.260, accuracy: 0.981\n",
            "Epoch: 50, loss: 0.236, accuracy: 0.981\n",
            "Epoch: 60, loss: 0.217, accuracy: 0.981\n",
            "Epoch: 70, loss: 0.203, accuracy: 0.981\n",
            "Epoch: 80, loss: 0.191, accuracy: 0.981\n",
            "Epoch: 90, loss: 0.181, accuracy: 0.981\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zHB-mHA1-Uh",
        "colab_type": "text"
      },
      "source": [
        "Since we're taking the argmax, we can just calculate logits since normalization won't change which index has the higher value. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpE3oDpIhhBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LogisticRegressionFromScratch():\n",
        "    def predict(self, x):\n",
        "        logits = np.dot(x, W) + b\n",
        "        exp_logits = np.exp(logits)\n",
        "        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n",
        "        return y_hat"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6T8rm1bw_Sw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Evaluation\n",
        "model = LogisticRegressionFromScratch()\n",
        "logits_train = model.predict(standardized_X_train)\n",
        "pred_train = np.argmax(logits_train, axis=1)\n",
        "logits_test = model.predict(standardized_X_test)\n",
        "pred_test = np.argmax(logits_test, axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Smy3DxJazGFm",
        "colab_type": "code",
        "outputId": "b04fa824-0491-4a88-906a-3bf1a0cb6dc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Training and test accuracy\n",
        "train_acc =  np.mean(np.equal(y_train, pred_train))\n",
        "test_acc = np.mean(np.equal(y_test, pred_test))\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 0.98, test acc: 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv-Dxp4AxeWh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_multiclass_decision_boundary(model, X, y, savefig_fp=None):\n",
        "    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n",
        "\n",
        "    Arguments:\n",
        "        model {function} -- trained model with function model.predict(x_in).\n",
        "        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n",
        "        y {numpy.ndarray} -- 1D outputs with shape (N,).\n",
        "    \"\"\"\n",
        "    # Axis boundaries\n",
        "    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n",
        "                         np.linspace(y_min, y_max, 101))\n",
        "\n",
        "    # Create predictions\n",
        "    x_in = np.c_[xx.ravel(), yy.ravel()]\n",
        "    y_pred = model.predict(x_in)\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "\n",
        "    # Plot decision boundary\n",
        "    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())\n",
        "\n",
        "    # Plot\n",
        "    if savefig_fp:\n",
        "        plt.savefig(savefig_fp, format='png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7RorJHHxfW7",
        "colab_type": "code",
        "outputId": "10f7e03f-d320-4b4a-c679-98126b61fb13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BbYSck3H/N/M1EdnFd1dgtBtgcdcKyUqMNsOmn28FgiQ0XAojhMDZb4ez3w4A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xb+7CaKVcXmciKlJDg+RC4aN3V/6HvhXEuP03wsyead2aaPm7HvjcBBjqHrZMJRMuEoqs1KYHTw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\n1l0PCAZ2yMk1swZzn13H2eglsRzcfqGCgjnB8cUVA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fwMM5NZ33SkYWCk00x9aKVOpMJR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\n8/8DOqPEog5O37IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QW5piG-XCz1N",
        "colab_type": "text"
      },
      "source": [
        "Credit for the plotting functions and the intuition behind all this is due to [CS231n](http://cs231n.github.io/neural-networks-case-study/), one of the best courses for machine learning. Now let's implement logistic regression with TensorFlow + Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Cy3efR7s7HD",
        "colab_type": "text"
      },
      "source": [
        "# TensorFlow + Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CuGUAtHkyFBv",
        "colab_type": "text"
      },
      "source": [
        "We will be using [Dense](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Dense) layers to recreate the same model and use [SparseCategoricalCrossentropy](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy) as our loss function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-vm9AZm1_f9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iII1ljErs2dL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Logistic regression\n",
        "class LogisticRegression(Model):\n",
        "    def __init__(self, num_classes):\n",
        "        super(LogisticRegression, self).__init__()\n",
        "        self.fc1 = Dense(units=num_classes, activation='softmax')\n",
        "        \n",
        "    def call(self, x_in, training=False):\n",
        "        \"\"\"Forward pass.\"\"\"\n",
        "        y_pred = self.fc1(x_in)\n",
        "        return y_pred\n",
        "    \n",
        "    def sample(self, input_shape):\n",
        "        x_in = Input(shape=input_shape)\n",
        "        return Model(inputs=x_in, outputs=self.call(x_in)).summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0e8U9NNluYVp",
        "colab_type": "code",
        "outputId": "7babcfbb-9251-4864-ee3e-ba570e8fd9c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "# Initialize the model\n",
        "model = LogisticRegression(num_classes=NUM_CLASSES)\n",
        "model.sample(input_shape=(INPUT_DIM,))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 2)]               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 6         \n",
            "=================================================================\n",
            "Total params: 6\n",
            "Trainable params: 6\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA7Oz97NAe8A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile\n",
        "model.compile(optimizer=Adam(lr=LEARNING_RATE),\n",
        "              loss=SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBWtqKCDtXHN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0cc9f60a-f13d-4a2d-cca8-5edce3c4ec78"
      },
      "source": [
        "# Training\n",
        "model.fit(x=standardized_X_train, \n",
        "          y=y_train,\n",
        "          validation_data=(standardized_X_val, y_val),\n",
        "          epochs=NUM_EPOCHS,\n",
        "          batch_size=BATCH_SIZE,\n",
        "          class_weight=class_weights,\n",
        "          shuffle=False,\n",
        "          verbose=1)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 722 samples, validate on 128 samples\n",
            "Epoch 1/100\n",
            "722/722 [==============================] - 1s 2ms/sample - loss: 8.3439e-04 - accuracy: 0.8615 - val_loss: 4.1483e-04 - val_accuracy: 0.9688\n",
            "Epoch 2/100\n",
            "722/722 [==============================] - 0s 72us/sample - loss: 2.5897e-04 - accuracy: 0.9834 - val_loss: 2.8280e-04 - val_accuracy: 0.9766\n",
            "Epoch 3/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 1.9570e-04 - accuracy: 0.9861 - val_loss: 2.3468e-04 - val_accuracy: 0.9844\n",
            "Epoch 4/100\n",
            "722/722 [==============================] - 0s 94us/sample - loss: 1.7173e-04 - accuracy: 0.9875 - val_loss: 2.1131e-04 - val_accuracy: 0.9844\n",
            "Epoch 5/100\n",
            "722/722 [==============================] - 0s 73us/sample - loss: 1.5492e-04 - accuracy: 0.9875 - val_loss: 1.9432e-04 - val_accuracy: 0.9844\n",
            "Epoch 6/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 1.4234e-04 - accuracy: 0.9889 - val_loss: 1.8063e-04 - val_accuracy: 0.9844\n",
            "Epoch 7/100\n",
            "722/722 [==============================] - 0s 72us/sample - loss: 1.3253e-04 - accuracy: 0.9889 - val_loss: 1.6964e-04 - val_accuracy: 0.9844\n",
            "Epoch 8/100\n",
            "722/722 [==============================] - 0s 71us/sample - loss: 1.2462e-04 - accuracy: 0.9889 - val_loss: 1.6068e-04 - val_accuracy: 0.9844\n",
            "Epoch 9/100\n",
            "722/722 [==============================] - 0s 70us/sample - loss: 1.1810e-04 - accuracy: 0.9889 - val_loss: 1.5321e-04 - val_accuracy: 0.9844\n",
            "Epoch 10/100\n",
            "722/722 [==============================] - 0s 68us/sample - loss: 1.1262e-04 - accuracy: 0.9889 - val_loss: 1.4687e-04 - val_accuracy: 0.9844\n",
            "Epoch 11/100\n",
            "722/722 [==============================] - 0s 66us/sample - loss: 1.0796e-04 - accuracy: 0.9889 - val_loss: 1.4143e-04 - val_accuracy: 0.9844\n",
            "Epoch 12/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 1.0394e-04 - accuracy: 0.9889 - val_loss: 1.3671e-04 - val_accuracy: 0.9844\n",
            "Epoch 13/100\n",
            "722/722 [==============================] - 0s 69us/sample - loss: 1.0043e-04 - accuracy: 0.9903 - val_loss: 1.3258e-04 - val_accuracy: 0.9844\n",
            "Epoch 14/100\n",
            "722/722 [==============================] - 0s 67us/sample - loss: 9.7343e-05 - accuracy: 0.9903 - val_loss: 1.2893e-04 - val_accuracy: 0.9844\n",
            "Epoch 15/100\n",
            "722/722 [==============================] - 0s 69us/sample - loss: 9.4607e-05 - accuracy: 0.9903 - val_loss: 1.2568e-04 - val_accuracy: 0.9844\n",
            "Epoch 16/100\n",
            "722/722 [==============================] - 0s 72us/sample - loss: 9.2165e-05 - accuracy: 0.9903 - val_loss: 1.2278e-04 - val_accuracy: 0.9844\n",
            "Epoch 17/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 8.9973e-05 - accuracy: 0.9903 - val_loss: 1.2016e-04 - val_accuracy: 0.9844\n",
            "Epoch 18/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 8.7994e-05 - accuracy: 0.9903 - val_loss: 1.1780e-04 - val_accuracy: 0.9844\n",
            "Epoch 19/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 8.6200e-05 - accuracy: 0.9903 - val_loss: 1.1565e-04 - val_accuracy: 0.9844\n",
            "Epoch 20/100\n",
            "722/722 [==============================] - 0s 76us/sample - loss: 8.4566e-05 - accuracy: 0.9903 - val_loss: 1.1369e-04 - val_accuracy: 0.9844\n",
            "Epoch 21/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 8.3072e-05 - accuracy: 0.9903 - val_loss: 1.1190e-04 - val_accuracy: 0.9844\n",
            "Epoch 22/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 8.1703e-05 - accuracy: 0.9903 - val_loss: 1.1025e-04 - val_accuracy: 0.9844\n",
            "Epoch 23/100\n",
            "722/722 [==============================] - 0s 76us/sample - loss: 8.0443e-05 - accuracy: 0.9903 - val_loss: 1.0873e-04 - val_accuracy: 0.9844\n",
            "Epoch 24/100\n",
            "722/722 [==============================] - 0s 88us/sample - loss: 7.9280e-05 - accuracy: 0.9903 - val_loss: 1.0732e-04 - val_accuracy: 0.9844\n",
            "Epoch 25/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 7.8205e-05 - accuracy: 0.9903 - val_loss: 1.0602e-04 - val_accuracy: 0.9844\n",
            "Epoch 26/100\n",
            "722/722 [==============================] - 0s 76us/sample - loss: 7.7209e-05 - accuracy: 0.9903 - val_loss: 1.0482e-04 - val_accuracy: 0.9844\n",
            "Epoch 27/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 7.6283e-05 - accuracy: 0.9917 - val_loss: 1.0369e-04 - val_accuracy: 0.9844\n",
            "Epoch 28/100\n",
            "722/722 [==============================] - 0s 73us/sample - loss: 7.5420e-05 - accuracy: 0.9917 - val_loss: 1.0264e-04 - val_accuracy: 0.9844\n",
            "Epoch 29/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 7.4616e-05 - accuracy: 0.9917 - val_loss: 1.0166e-04 - val_accuracy: 0.9844\n",
            "Epoch 30/100\n",
            "722/722 [==============================] - 0s 71us/sample - loss: 7.3865e-05 - accuracy: 0.9917 - val_loss: 1.0074e-04 - val_accuracy: 0.9844\n",
            "Epoch 31/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 7.3162e-05 - accuracy: 0.9917 - val_loss: 9.9879e-05 - val_accuracy: 0.9844\n",
            "Epoch 32/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 7.2503e-05 - accuracy: 0.9917 - val_loss: 9.9070e-05 - val_accuracy: 0.9844\n",
            "Epoch 33/100\n",
            "722/722 [==============================] - 0s 88us/sample - loss: 7.1885e-05 - accuracy: 0.9917 - val_loss: 9.8309e-05 - val_accuracy: 0.9844\n",
            "Epoch 34/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 7.1303e-05 - accuracy: 0.9917 - val_loss: 9.7592e-05 - val_accuracy: 0.9844\n",
            "Epoch 35/100\n",
            "722/722 [==============================] - 0s 90us/sample - loss: 7.0757e-05 - accuracy: 0.9917 - val_loss: 9.6916e-05 - val_accuracy: 0.9844\n",
            "Epoch 36/100\n",
            "722/722 [==============================] - 0s 83us/sample - loss: 7.0242e-05 - accuracy: 0.9917 - val_loss: 9.6277e-05 - val_accuracy: 0.9844\n",
            "Epoch 37/100\n",
            "722/722 [==============================] - 0s 72us/sample - loss: 6.9756e-05 - accuracy: 0.9917 - val_loss: 9.5673e-05 - val_accuracy: 0.9844\n",
            "Epoch 38/100\n",
            "722/722 [==============================] - 0s 92us/sample - loss: 6.9298e-05 - accuracy: 0.9917 - val_loss: 9.5102e-05 - val_accuracy: 0.9844\n",
            "Epoch 39/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 6.8865e-05 - accuracy: 0.9917 - val_loss: 9.4561e-05 - val_accuracy: 0.9844\n",
            "Epoch 40/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 6.8455e-05 - accuracy: 0.9917 - val_loss: 9.4047e-05 - val_accuracy: 0.9844\n",
            "Epoch 41/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 6.8067e-05 - accuracy: 0.9917 - val_loss: 9.3560e-05 - val_accuracy: 0.9844\n",
            "Epoch 42/100\n",
            "722/722 [==============================] - 0s 71us/sample - loss: 6.7700e-05 - accuracy: 0.9917 - val_loss: 9.3097e-05 - val_accuracy: 0.9844\n",
            "Epoch 43/100\n",
            "722/722 [==============================] - 0s 70us/sample - loss: 6.7352e-05 - accuracy: 0.9917 - val_loss: 9.2657e-05 - val_accuracy: 0.9844\n",
            "Epoch 44/100\n",
            "722/722 [==============================] - 0s 76us/sample - loss: 6.7022e-05 - accuracy: 0.9917 - val_loss: 9.2238e-05 - val_accuracy: 0.9844\n",
            "Epoch 45/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 6.6709e-05 - accuracy: 0.9917 - val_loss: 9.1839e-05 - val_accuracy: 0.9844\n",
            "Epoch 46/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 6.6411e-05 - accuracy: 0.9917 - val_loss: 9.1459e-05 - val_accuracy: 0.9844\n",
            "Epoch 47/100\n",
            "722/722 [==============================] - 0s 86us/sample - loss: 6.6129e-05 - accuracy: 0.9917 - val_loss: 9.1096e-05 - val_accuracy: 0.9844\n",
            "Epoch 48/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.5860e-05 - accuracy: 0.9917 - val_loss: 9.0750e-05 - val_accuracy: 0.9844\n",
            "Epoch 49/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.5604e-05 - accuracy: 0.9917 - val_loss: 9.0420e-05 - val_accuracy: 0.9844\n",
            "Epoch 50/100\n",
            "722/722 [==============================] - 0s 83us/sample - loss: 6.5360e-05 - accuracy: 0.9917 - val_loss: 9.0105e-05 - val_accuracy: 0.9844\n",
            "Epoch 51/100\n",
            "722/722 [==============================] - 0s 83us/sample - loss: 6.5129e-05 - accuracy: 0.9917 - val_loss: 8.9803e-05 - val_accuracy: 0.9844\n",
            "Epoch 52/100\n",
            "722/722 [==============================] - 0s 84us/sample - loss: 6.4908e-05 - accuracy: 0.9917 - val_loss: 8.9515e-05 - val_accuracy: 0.9844\n",
            "Epoch 53/100\n",
            "722/722 [==============================] - 0s 88us/sample - loss: 6.4697e-05 - accuracy: 0.9917 - val_loss: 8.9239e-05 - val_accuracy: 0.9844\n",
            "Epoch 54/100\n",
            "722/722 [==============================] - 0s 85us/sample - loss: 6.4496e-05 - accuracy: 0.9917 - val_loss: 8.8975e-05 - val_accuracy: 0.9844\n",
            "Epoch 55/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 6.4305e-05 - accuracy: 0.9917 - val_loss: 8.8723e-05 - val_accuracy: 0.9844\n",
            "Epoch 56/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.4122e-05 - accuracy: 0.9917 - val_loss: 8.8480e-05 - val_accuracy: 0.9844\n",
            "Epoch 57/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 6.3947e-05 - accuracy: 0.9917 - val_loss: 8.8248e-05 - val_accuracy: 0.9844\n",
            "Epoch 58/100\n",
            "722/722 [==============================] - 0s 72us/sample - loss: 6.3781e-05 - accuracy: 0.9917 - val_loss: 8.8026e-05 - val_accuracy: 0.9844\n",
            "Epoch 59/100\n",
            "722/722 [==============================] - 0s 73us/sample - loss: 6.3622e-05 - accuracy: 0.9917 - val_loss: 8.7812e-05 - val_accuracy: 0.9844\n",
            "Epoch 60/100\n",
            "722/722 [==============================] - 0s 73us/sample - loss: 6.3469e-05 - accuracy: 0.9917 - val_loss: 8.7607e-05 - val_accuracy: 0.9844\n",
            "Epoch 61/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 6.3324e-05 - accuracy: 0.9917 - val_loss: 8.7411e-05 - val_accuracy: 0.9844\n",
            "Epoch 62/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 6.3185e-05 - accuracy: 0.9917 - val_loss: 8.7222e-05 - val_accuracy: 0.9844\n",
            "Epoch 63/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 6.3052e-05 - accuracy: 0.9917 - val_loss: 8.7041e-05 - val_accuracy: 0.9844\n",
            "Epoch 64/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 6.2925e-05 - accuracy: 0.9917 - val_loss: 8.6866e-05 - val_accuracy: 0.9844\n",
            "Epoch 65/100\n",
            "722/722 [==============================] - 0s 75us/sample - loss: 6.2803e-05 - accuracy: 0.9917 - val_loss: 8.6699e-05 - val_accuracy: 0.9844\n",
            "Epoch 66/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 6.2687e-05 - accuracy: 0.9917 - val_loss: 8.6538e-05 - val_accuracy: 0.9844\n",
            "Epoch 67/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 6.2576e-05 - accuracy: 0.9917 - val_loss: 8.6383e-05 - val_accuracy: 0.9844\n",
            "Epoch 68/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 6.2469e-05 - accuracy: 0.9917 - val_loss: 8.6234e-05 - val_accuracy: 0.9844\n",
            "Epoch 69/100\n",
            "722/722 [==============================] - 0s 84us/sample - loss: 6.2367e-05 - accuracy: 0.9917 - val_loss: 8.6091e-05 - val_accuracy: 0.9844\n",
            "Epoch 70/100\n",
            "722/722 [==============================] - 0s 88us/sample - loss: 6.2269e-05 - accuracy: 0.9917 - val_loss: 8.5953e-05 - val_accuracy: 0.9844\n",
            "Epoch 71/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 6.2175e-05 - accuracy: 0.9917 - val_loss: 8.5821e-05 - val_accuracy: 0.9844\n",
            "Epoch 72/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 6.2085e-05 - accuracy: 0.9917 - val_loss: 8.5693e-05 - val_accuracy: 0.9844\n",
            "Epoch 73/100\n",
            "722/722 [==============================] - 0s 92us/sample - loss: 6.1999e-05 - accuracy: 0.9917 - val_loss: 8.5570e-05 - val_accuracy: 0.9844\n",
            "Epoch 74/100\n",
            "722/722 [==============================] - 0s 87us/sample - loss: 6.1917e-05 - accuracy: 0.9917 - val_loss: 8.5452e-05 - val_accuracy: 0.9844\n",
            "Epoch 75/100\n",
            "722/722 [==============================] - 0s 100us/sample - loss: 6.1838e-05 - accuracy: 0.9917 - val_loss: 8.5338e-05 - val_accuracy: 0.9844\n",
            "Epoch 76/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.1762e-05 - accuracy: 0.9917 - val_loss: 8.5228e-05 - val_accuracy: 0.9844\n",
            "Epoch 77/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.1690e-05 - accuracy: 0.9917 - val_loss: 8.5122e-05 - val_accuracy: 0.9844\n",
            "Epoch 78/100\n",
            "722/722 [==============================] - 0s 87us/sample - loss: 6.1620e-05 - accuracy: 0.9917 - val_loss: 8.5020e-05 - val_accuracy: 0.9844\n",
            "Epoch 79/100\n",
            "722/722 [==============================] - 0s 84us/sample - loss: 6.1553e-05 - accuracy: 0.9917 - val_loss: 8.4922e-05 - val_accuracy: 0.9844\n",
            "Epoch 80/100\n",
            "722/722 [==============================] - 0s 71us/sample - loss: 6.1489e-05 - accuracy: 0.9917 - val_loss: 8.4827e-05 - val_accuracy: 0.9844\n",
            "Epoch 81/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 6.1428e-05 - accuracy: 0.9917 - val_loss: 8.4736e-05 - val_accuracy: 0.9844\n",
            "Epoch 82/100\n",
            "722/722 [==============================] - 0s 99us/sample - loss: 6.1369e-05 - accuracy: 0.9917 - val_loss: 8.4648e-05 - val_accuracy: 0.9844\n",
            "Epoch 83/100\n",
            "722/722 [==============================] - 0s 88us/sample - loss: 6.1313e-05 - accuracy: 0.9917 - val_loss: 8.4563e-05 - val_accuracy: 0.9844\n",
            "Epoch 84/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 6.1258e-05 - accuracy: 0.9917 - val_loss: 8.4481e-05 - val_accuracy: 0.9844\n",
            "Epoch 85/100\n",
            "722/722 [==============================] - 0s 87us/sample - loss: 6.1206e-05 - accuracy: 0.9917 - val_loss: 8.4402e-05 - val_accuracy: 0.9844\n",
            "Epoch 86/100\n",
            "722/722 [==============================] - 0s 81us/sample - loss: 6.1157e-05 - accuracy: 0.9917 - val_loss: 8.4325e-05 - val_accuracy: 0.9844\n",
            "Epoch 87/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 6.1109e-05 - accuracy: 0.9917 - val_loss: 8.4252e-05 - val_accuracy: 0.9844\n",
            "Epoch 88/100\n",
            "722/722 [==============================] - 0s 77us/sample - loss: 6.1063e-05 - accuracy: 0.9917 - val_loss: 8.4181e-05 - val_accuracy: 0.9844\n",
            "Epoch 89/100\n",
            "722/722 [==============================] - 0s 82us/sample - loss: 6.1019e-05 - accuracy: 0.9917 - val_loss: 8.4112e-05 - val_accuracy: 0.9844\n",
            "Epoch 90/100\n",
            "722/722 [==============================] - 0s 78us/sample - loss: 6.0976e-05 - accuracy: 0.9917 - val_loss: 8.4046e-05 - val_accuracy: 0.9844\n",
            "Epoch 91/100\n",
            "722/722 [==============================] - 0s 76us/sample - loss: 6.0936e-05 - accuracy: 0.9917 - val_loss: 8.3982e-05 - val_accuracy: 0.9844\n",
            "Epoch 92/100\n",
            "722/722 [==============================] - 0s 74us/sample - loss: 6.0897e-05 - accuracy: 0.9917 - val_loss: 8.3921e-05 - val_accuracy: 0.9844\n",
            "Epoch 93/100\n",
            "722/722 [==============================] - 0s 87us/sample - loss: 6.0860e-05 - accuracy: 0.9917 - val_loss: 8.3861e-05 - val_accuracy: 0.9844\n",
            "Epoch 94/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 6.0824e-05 - accuracy: 0.9917 - val_loss: 8.3804e-05 - val_accuracy: 0.9844\n",
            "Epoch 95/100\n",
            "722/722 [==============================] - 0s 83us/sample - loss: 6.0789e-05 - accuracy: 0.9917 - val_loss: 8.3748e-05 - val_accuracy: 0.9844\n",
            "Epoch 96/100\n",
            "722/722 [==============================] - 0s 79us/sample - loss: 6.0756e-05 - accuracy: 0.9917 - val_loss: 8.3695e-05 - val_accuracy: 0.9844\n",
            "Epoch 97/100\n",
            "722/722 [==============================] - 0s 80us/sample - loss: 6.0725e-05 - accuracy: 0.9917 - val_loss: 8.3643e-05 - val_accuracy: 0.9844\n",
            "Epoch 98/100\n",
            "722/722 [==============================] - 0s 87us/sample - loss: 6.0694e-05 - accuracy: 0.9917 - val_loss: 8.3594e-05 - val_accuracy: 0.9844\n",
            "Epoch 99/100\n",
            "722/722 [==============================] - 0s 92us/sample - loss: 6.0665e-05 - accuracy: 0.9917 - val_loss: 8.3545e-05 - val_accuracy: 0.9844\n",
            "Epoch 100/100\n",
            "722/722 [==============================] - 0s 86us/sample - loss: 6.0637e-05 - accuracy: 0.9917 - val_loss: 8.3499e-05 - val_accuracy: 0.9844\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f382a72d630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dM7iYW8ANYjy",
        "colab_type": "text"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFXbczqu8Rno",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D3_dQmWdQhSm",
        "colab_type": "text"
      },
      "source": [
        "### Components"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RnQrNaKQhWJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, classes, cmap=plt.cm.Blues):\n",
        "    \"\"\"Plot a confusion matrix using ground truth and predictions.\"\"\"\n",
        "    # Confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    #  Figure\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(111)\n",
        "    cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "    fig.colorbar(cax)\n",
        "\n",
        "    # Axis\n",
        "    plt.title(\"Confusion matrix\")\n",
        "    plt.ylabel(\"True label\")\n",
        "    plt.xlabel(\"Predicted label\")\n",
        "    ax.set_xticklabels([''] + classes)\n",
        "    ax.set_yticklabels([''] + classes)\n",
        "    ax.xaxis.set_label_position('bottom') \n",
        "    ax.xaxis.tick_bottom()\n",
        "\n",
        "    # Values\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, f\"{cm[i, j]:d} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    # Display\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klyws2fhBeyr",
        "colab_type": "text"
      },
      "source": [
        "### Operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-jZtTd7F6_ps",
        "colab_type": "code",
        "outputId": "755810f7-3181-4e72-9fe8-ab79303fc85c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Predictions\n",
        "pred_train = model.predict(standardized_X_train) \n",
        "pred_test = model.predict(standardized_X_test)\n",
        "print (f\"sample probability: {pred_test[0]}\")\n",
        "pred_train = np.argmax(pred_train, axis=1)\n",
        "pred_test = np.argmax(pred_test, axis=1)\n",
        "print (f\"sample class: {pred_test[0]}\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample probability: [2.5153426e-05 9.9997485e-01]\n",
            "sample class: 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEjansj78Rqe",
        "colab_type": "code",
        "outputId": "a8443219-3fee-4535-a7d9-25dacd0f64ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Accuracy\n",
        "train_acc = accuracy_score(y_train, pred_train)\n",
        "test_acc = accuracy_score(y_test, pred_test)\n",
        "print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train acc: 0.99, test acc: 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9uDMe_Dh4cs",
        "colab_type": "code",
        "outputId": "821ab91e-952f-4b57-8a9c-98ce8d57aaa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "# Visualize the decision boundary\n",
        "plt.figure(figsize=(12,5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title(\"Train\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_train, y=y_train)\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title(\"Test\")\n",
        "plot_multiclass_decision_boundary(model=model, X=standardized_X_test, y=y_test)\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g8GkIyUAKskmG/2wH2kd9B/wKCOJ4QiKYOPIIITB/ecm44aOM2WFC7wX/oS72jDF0n++C\n/1wntBKHrEg1HsCtgAQwQRwfDNc3AQhNYPbtRcgfyBh8shftwztb2ywOM4Y/039A0RLEyeL475ET\nJ55ipgQ137jRTAigfdTXkppnxpju9HAEBPAGr30rTgKYII4BxUwJpQbrm1bQsHB5GYkVcncg9FII\nLlRAcKjvvkmlEPuEMsHEkYdJbNuyVjWvQs2rMNma15AmhEBsMYHQnTDUogZvnxv+c50wWeljQxBE\nc2ASq1vitQHXBFY+DsJDAy9ONbTL13zo25w48pjtJljdFuTi+Yb3k+pMadsri1dWEJmLVxrwgqkw\nwrMxnHtlAmY7uT8QBLF/zHYTrB4LcrHG61s+WTikiIijyEajMwng5kLlEMSxYPS5Qcj1ph0xwN3j\nrOvIkE/kEbobRngmuu0Qi82PCc/GqhwoBBdQCypWPwkin8jv+FwEQRCN2FjfGpVUke0icenlNKZM\nHqR+PNfqUE4MlAkm9o0QAqWcCklmUCwH8ydl81rx8DemELwdQvBWuFweIcAkCWa7CcNPDxjGtfD+\nCiJzum8mY8DClRWMPjsA32Bj8/9EIG28RSmA8HQU0fk4BBfw9rsx/HT/kbBEIwjieGLz6OtbZDaK\nlY+D4Fu8ziWZofdhf4uiI1pN54cf4PEf/Tmc/0sEP+hyYviMHZJ/6MT4/reSfSsWxtgAgD8B4Icu\nG74rhPhn+z0vcTxIrqUx/94yStkSAMDRbsPIs4NNtSkrpIsI3Q0jG83B5rHi7CsTKCQLKKSLsHqs\ncPc4DReD6Hwc0fkHo403NO3sO0u40OVsWNsryQysQaneRoY4vpzEzFsLOPPS6H5eYtMgb2CCOJ4o\nZhn+qU74Br2YeWsB2ViuUi/ce6ELbcM0te804r/yLs58//+BXCoCAPJrKdwPp+Edv4fuC5Mtju74\n04y0nQrgd4QQHzLGXACuMcZ+JoS41YRzE0eYXDyve1tusvZJh7O4/ZNpPPyNKch7rNEVQiC+lETw\n9jqK2RJK2RLKiV+kQhmEZ6IYf3EE/rOdDc8TuhMGVw3EoNAFsn+qo+5jvQMeLF5d3T5WLpAKZVBI\nFWBxWba9fz1KuRKCt8NIrKagWGR0TXXA2+/e1ZX+Rtcw1YwRxPHFbDfh7JfGUUgXoRZU2DzWpvc7\nEMcEzjH2w39fEcAbaKpA9N48Os+OQjZRmcx+2PcnSwgREEJ8WP53CsBtAH37PS9x9AncDOnm7psR\nepY0thDf83mXrgUw9+4S0utZFDMlPam58TRC75See2cRYptsp1rUDG8XXECrc2wDk1XB8Gf6wWS2\nrfUZkxjyqWLD+zSimCni5l/dQ/BOGLl4HqlgBnPvLGH5WmDH5whkYySACeIEYXGa4Wi3kwA+xZiy\nGSi5rOExJkkoJNOHHNHJo6mfLsbYMIBHAbxvcOy3GGNXGWNX8+m9CyTi6JCN5gzrBbjKt3VyqEch\nXcT6/YjhSOTNaEVt225pT5/L8C9cUiS4/NuPVm4f8eHhX59C70U/us93QjYZf1wEFzDvo/xj9ZMg\n1KIGsemCgqscofsRFNI7F9fkDUwQBHFyUC1WiDqDHQXnUKw0Cnu/NE0EM8acAP4DgH8ohKhx9RZC\nfFcI8YQQ4gmrk2qbTgL1ZtFLCoPVvbfSgGQghZ1WAGxXKtB9rguKSa4aDstkBkeHHc6u7UUwoG9N\n9pzvQv+jPeh9pBuSXPucQhO4+aO7uPfzWRRSu7cxii8n6xYfJ1dTuz4fQRAEcfwRJhPmH3scqrKl\ncpUx2No8MDtsrQnsBNEUEcwYM0EXwH8qhPiLZpyTONoIIepaljFJ2nMTh771t70KNtlMsLgaZ1/N\ndhPOvTKBjlEfFKsCi9OM3gt+TLw4vKeu2q4z7eh52A9JkWq3KAWQDKRx46/uId8ge6sVNYRnoli7\ntY5MRN/mYrLxx5AxBlZ+HrWgYv1+BGu31/ecZScIgmgE1zgiczHMXV7C8vXAni7qieYRzCVx7atf\nQ9uLQ5DMEiRFAZNlWL0uDDzzaKvDOxE0wx2CAfhDALeFEP94/yERx4HgnTCiZeuxzUgKw9QXx3Zl\nGSaEQCaSQyFZgGI3NXQ2YDIDYwyjzw3uSMiaHWZD+7S9wBhDz0Nd8J/twPKHqwjdi9ZkcIUmcOuv\n7uGhr0/WDNRIBlKYfnMBgIDgAowxuLqdaB/1IXh7veJiUTmX0C3YogtxzL27BAb9rVm5vgbfoAcj\nzwyQRQ5BEE1BLWq4/ZP7KGVVvRxNAoK3wxh5eqAqqSG4QHw5icRqEopFQcdY2553/ojteeIrRbzy\n97+Fq3/rr1FIpGBy2GHz0eTAZtEMd4hnAfxdAJ8yxq6Xb/u2EOKvm3Bu4ggihMDajVCVK8SDYzAs\nGciEs/rwCY3DN+CBp9cFJjGU8mq5jKBYOYFiM6GUK+lOX1yAyQwmiwx7hwN2nxUdoz7kU0VE5+Nw\ndNibase2EyRZQj5ZrFvCwFWOletrGHnmgfjWVI7pNxeqh29AILmWhrPDDrvPhlw8D65yvRGPASPP\nDEBoHHPvLkFoourp4osJRHpc6Bj1HdCrJAjiNLFyfQ3FdOlBbwLX16j5y0vw9Lkgm2RoKsedn06j\nkCrqaxnTEyKDT/Sic6K9tS/ghGNxOWBx7ayMj9g5+xbBQoi3sZP9a+LEoE9OM3ZX2HBK2GwXtvxR\nQLcrK4vm2EIC9jYbus60Y+1WeXt/k8IrZYtwdjng7fdALahwdTng6ta9gLOxHG7/ZBpa2UxecIG2\nEa/u5HCIWVGLywIE6nfmxpcSAB6I4MRyTZk8AD1zvD4dxcPfmEIykEZyLQXFoqB9xAez3YTgnbDh\n47gmELoTRseoj7yBCYLYNxsDgGpgDInVFNqGvFi7GUI+UXhwP6GvYYtXV+Ed8DT0XieIowj9xRK7\nhkkMikU2FMKCi6qtsWwsh+CdcNVWP1c50qEM0usZw2yq4EA6lMXY80OVCXRCCGglDXdfn4W25Xlj\n83HYvdZtfYP3ixD6NmB0LqaLcIb60zQYQzqchdlugtluglbU6lq6aSUOxhg8vS54el1bjmk1ZRKb\nj214A1/6QhKTihufkjMEQRB7oJHl5IboDc/EDIUyYwzx5SQ6x9sOLD6COAhIBBO7hjGGrnOdCHy8\nBrHJyYxJDO5uZ1V5QmwhYZxdAOoLyPK51KIG2Sxj/V4Eq58GoeaNs89cEwjeCR+oCBZcYPrNeaSC\nmW3t2wC9Ae7ez2chuICry4G+R7rrfsk42ut3+Lq7nVi7uV7znEwCTH5LlTfwp6+RACYIYm94+92I\nzsdr+xy4gLunfHFeZw0TQtRf5wniCEMu3MSuSAXT+PSHdxD4OFhZDzcGSvgG3Rh9fqjq/pyLhmK3\nHkxisDjMCN5ax/KHgboCeIN65RnNIr6cRCqY3lYAb67I4CUOoQmkghksfRiAJBl/3Eo5te757O02\nWD2W6oIjBsgmGc4JF3kDEwTRFPof6YZilsE29XRIMkPfI/5KmYNvwANmtIwJ1OxiEftjY5evKtNE\nNB3KBBM7JhvL4f4vqsckg+kz78++MgGzrXZ8o2/Qg/W7YcMmunpIMkP/o3rmdLVOA95WGmVTm0Fk\nNmY8grmM2WGCo8Oue/5udXngAun1DFid0vlcIo9SXjWsp1v8YBW5ePVQEsUsY+pL44jJ1ZOEuKpC\nK6lQrBZyjSAIYleYHWY89PVJhO6GkQikYbaZ0DXVAdcmT/WeC37ElpNQC2qlTEtSGLqmOg+9Qfkk\ns7nM7dvjDnz6W2+2OqQTC4ngU4gQAtloDoIL2NtskOr41G4lYCRIhV7Tmg5l0DZU6w3saLfBN+RF\nbDGxfRZVYrC4zOi72A3foEeflraDLTZJZuh7tGdHr2HPbKMpS3kVPQ91IbaQMH44Q93aXr25pPa9\nSYeziM7GILYkubnK9SEaA7oNXSmrYunyp0itBAEAkkmB/8IkfCP9jYMmCILYhGJR0HuhG70XjI+b\nrArOf/UM1u9FEF/RLdL8k+0PyiWIfbNZAP/u+jUqcztgSASfMtLrGcy8tfCgsQvA0FN9aB/Z3mor\nG80Z3l4ZkzxUe4wxhuGn+/WM8HQUal6tCPDKfSQGm8+Ks18ar8pgKha5seEBA5wddvQ/1gNnh73m\ncCFdRCFdhNVtqfHs3S2eXhfiS8YODwAgSQypYKbu8a1CdiuFTBFmR3UmJbYYN8yCc00gMB1G10AX\nJmUXfvrf/wqptXzlPdUKRQQ+vAnZpMDd3934iQmCIHaBYpbR81AXeh7qanUoJ5ZLL6fx7TEHCeBD\ngETwKUItqLj387majOzCe8uwui1wtNcKyc1YXZYHfr6bkBSp4VYYYwzefje8/brBdy6ex9K1VaSC\nGUgyQ/uYD30Xu2u28GWTjLZhL6IL8aosKpMYPH0ujL8wbPh8WlHD9FsLSK9nIEkMXBPwDrgx8szA\njrPegC76Vz5eQzqcrXGk2AovlzzsBSazOmVf9dPPLp+GH/62hqX/5l8gHizUNKUIjSN44x6JYIIg\nCIKoA4ngU0R4NmboUMA1gbVb6xh73iCVu4nuh7r05rAt2UkmMfgMSiHqYfNaceal0R3dd/CpPqhF\nDclAShe0ZbeFzYMotjL79iLSoQwEF9DKscaXk1i6toqhp3ZWIpAOZ3HvZzM7qkdmEoOzww6tuLfm\nPMYYHAaZ7LYh43pq2Qz81193Iv6d72Fh1g3GmGHvYSltnLknCIIgjiZcaAAEtMtvtTqUUwGJ4FNE\nIVmoW5dqlOHdiqvLgaFL/Vj8YEUvUxACJrsJ4y8MQ1YOxmhEViRMfG4YhXQR+VQBVqe5ahAHoDee\n5eJ5vUnPIiMZTBtkRgXCMzEMPN67o2zw0tXVnTXzMcDd48TIs4MI3YsgFcxsbxW0yV9YkhkGHu8x\nfP8c7XZ0jLchPBOrZO+ZzDBxhuHLrqu4vzYMkyNet2REsdEoU4IgiONCIBsDIPDqkIrU9+YADLc4\nopMPieBThKPDjshcvLZBjcEwE2lE+6gPviEPcvE8JEWC1X04TgQWp9mw5CKxmsLcO4sVwcpk1nCG\nhVbUINm2F8GZSHbb+4ABXVMdGHy8FwDQNdGG0O31+tP0ZIauM+3QVI7MehZmpxnd5zqruq+3MvBE\nL3yDHn3ktMrBuhV8+/fyMF3Tj9vaPDA77Sgk01UenkyW0XlufPvXcEwQQiAXiaOYycLidsHmc7c6\nJIIgdohaUKGVOMwOEznX1CGQjQIAfvjbGuLf+QGW1oZbG9ApgUTwKaJtyIuV62s1IliSJXSf2/mg\nCUmWtq0fPgzyyQJm3pyvztjWt9yFJEuVCXQbcC4Q+DSI9XsRaCUOR7sN/Y/1QFIk8NL2bhb+yY7K\nz4pFwdkvT2DxgxUkVlLV95UZzHYTei/4IZvkHb9GxhhcfidcficAPVMgy4Wq48MvPImlyx8hF0mA\nSRKEEOg8OwrvcN+On+coU8oVsPDmFZSyenmHEIDN58bg849DNu2v4ZEgiIOjlCth9p0lpEMZMAZI\nJhmDT/YaOgmdZoK5JC69nMK3x+yIf+d7JIAPERLBpwhJkXD2S+OYf38ZqUAaAoDdZ8PQpb4D9XjU\nShpiiwmoBQ3OTjscHXYwxpCL57H6SRDp9QxMNhO6z3XCN+TZNlOgqRzZaG5X/sOSzND7cBeYVH3u\n2bcWkAikKmUi6fUs7r0+C2eXA8lA2vBcG2byw5/pr3nfLE4zJl4cAecc0fk4QnciUPMqnF0O9D/e\nsysBvFMUqwUjL34GpWwOaqEIi8sJSWn+87SKpcsfoZBKV6X3c9E4Vq/dxMBnHmldYKecUl6FVlBh\ndpp31XBKnA6EELjzNzO61WV5ZhLXVMy/uwTFosDd7Wx1iEcO7fJbJIAPGRLBpwyzw4wznx8F1ziE\nwIHV8gohUMyWkIvlMfv2IgABrgk9i9xhR+8FP+7/YrYygKKUUzF/eQnZeB79j9R3NFi7tY7Vj9cA\niW2bqa3AgL7HetB1pr3q5lw8XyWAN+CaQKqB00PfI93oGGuDYq4vNBkYkqsp5BJ5AHpjXnw5ifHP\nDe968d8YSbqd0DDZbTDZD3ZoSD2KmRyCH99BKhACALj7/Oh+5CwU6/7qkkvZHPLRhOEo19RyEFzV\nTpTgPw6oBRVz7ywhuZbWLyoZ0HfBf6Bjy4njRzKQ1qdhbvnsck1g9ZMgiWDiSEAi+JRykJmbyHwM\nS1cD0IpqjfUXVznS6xnMvkJ+gt4AACAASURBVL1YM4GNawLBW+vwT3UYTk+LLSWw+vGanv3dxQQ6\nCKCYKeH+L+egWBR0nWmHs9OBTCSrD7Ewekid6XCSIsFkURoKYAAI3AwhtpiovH5RfpbpN+Zx8T86\nt6OLD62kYenqKiJzcQguoLgVeC960dg8+fBRC0XMvv4utMKD5srE0hoy6zFMfPl5SMrelxm1UNRL\nPLjxBY9WUkkEHzL3fjGHXCwHwVFpAl25vgbFoqB9dHu/cWL/5JMFrN1eRzaag81jhf9sB+y+1lwA\n1yOfrLVurBwrJwcIotWQCCaaSiKQwsLl5YZlCkITKGVLhseYxJBez8A34Kk5Fvh0ZyOUjQjeXq+o\n3fhiAj0P+2HzWYGGbXS1cJVj9dMgJEWCb7A2xmKmqFu0rddvrIsvJbYdTiKEwL3XZ5GNPRiCoSZV\nJK+EEfuf3sOtdNuOYz5oYjOL4KUtxdhCQCuWEF9YRdvY4J7PbXE5DW39AH0ynmKlUa2HSSaSRT6e\nr7241QRWPw2SCD4E0usZ3Pv5rL4WCt3PPLYQx+jzQxUv9qOAxWUGk5ihELa46+8QCS7AuYAkM2qi\nIw4cKuQimsrqx8E9C9UN6tXNFrPb27jVZVNIG9txNo8V0h7KQQqpIubeWcTqJ8Gq2zkXuPPTGaTD\n9QWwEGJHfsLp9SxyidpMSrEg8H9NP7zrmA+STChqmKkVmoZMKLKvc0uKjM5zY2By9d8EkyV0X5yi\nL8lDppAq6jPADShmjC9sieYyf3lZ30XbWBqEvqbNX17a3p7xEPH0uKBY5JqZP3p/hr/m/lzlWLiy\ngg+/fwMfff8GPv3LO4gtxg8p2tbCtxsp2gJKuTyy4RjUfGH7Ox9jSAQTTSWf3NkHxuww1TSpAfoC\nWc8yzO5t4nYf0+3Vzrw0AsWqVJrddgrXBAI3Q1ALDzKgiZUk1KK2bWLZ2cASbYOto6U3B75QPFr+\nvya71Xi4HWNNqVHumBpFz+PnYHbawSQJFrcT/Z955MS4XxwnrG5L3XIcs2PnTh2CC2SjOeQS+bqZ\nfqKWUl7VG80M4Jqo9CAcBZjEMPXFMTja7WASg6RIkM0yBp/sg6fXVXP/6bcWEJ6O6j0a5RK22XeW\nEF+pP67+JLDZG/gowFUNi+98iPv/35tY+NVV3PurN7D03nVw7egJ9WZA5RBEU7E4zchGG0wqY3o9\n8thnh7D4wSpy8Tw455AkCYwBE58fMRTHANB70Y/0eqYq01xvu207GPSElt1nw8XfPIv4agqzb87v\nqtxWL93IVrYg88kCuFa/WY/JDN4+945q9zYuEoxem08+GovlBm0TQ0gsBSC2vHYmMfhG60/22ymM\nMfiG++Eb3tm0P+LgsLfZYPPZai7SJJmh90Jtds+I2FIC85eX9ccLAcVmwtjzg0fCdvGo03DjQ2zf\nPHvYmB1mnP3SOIrZErSiBovbAslgfc8l8kjVGXK08tEavH1Hp8yjmWwI4Ne+GYP/93+AW0fAGWLl\ng0+RDqxDcF7Z4UutBBFQZPQ9cbR2IZvB0frEEE1BLWpIBdMtyQr0XvBDMsiqMgkw2RS0DXtx7pUJ\nONrtmPq1MUy8OIz+R3owdKkPF149V/eLsJQrwWw3YeyFIVhcZoCVRdagGxZ3/bpQd6/TUFQLAXjK\nCyuTGHz9bnSMt9VkhI1ey4OTAPKmBjmr21L3S4jJDP2PdGP0uZ3Vx3p6XYalGmbG8RVvdEfnOCxs\nPg96Hj0HJkuQFAWSIoPJMvqeugCLa/usN3G8mPj8CNy9rnJ2j0E2Seh7rGfbOndA3+GYe3sRWlED\nVzm4JlBMF3H39dmqXRXCGMWiwNFufBFtspn0tfEIYrabYPNaDQUwAORi+bqlTTvdXTxuBHNJAAI/\n/G0N/t8/GsMx1EIRqZVgTXmb0DgS86vg6sn7jFIm+AQhhMDqx0Gs3V7XhR8XsLgsGH9xGBbH4SyO\n3n43Bp7oxfKHAQihx+Ros2H0+SGY7dXbpVsHQRiRS+Qx984icvECwACzzYThZwZg91n1L2FZQnw5\nidlfLVTXIjPA5Xdg4LFeTL85j1JO1YeElMVz/yPdNfEMPNkHMCA8EwNjDEIIdEy0Q1M1RGfjNVkK\nSZHg7Hwg2j19bihmGUWN15REdI63oWOsrSLIOefIxwsQQsDus9UIdUmWMPnyKO7/ch6lXAlWiwAv\ncrziiOKSw9i/uJX4RgfgHuhBJhQBY4Cjq4NcG04oilnGxOeGoRZUqEUNZoe5rrjZytqtdeOeAS4Q\nmYvBP0U2a9sx/MwA7vxkGlzj4KrQp2RKDGOfHTIUklpJq6yVG3CNY/1+BOHpKLgm0Dbkhf9c57au\nNwdFo1IaxcAp6KTw2rfi0C5/dCQEMACo+UJ5B9LgIGNQC0WY9+H2cxQ5Wa/mlBOeiWLtVki3Lip/\n0eQSedz92SzOf3UCqWAGQhNw+R01k9OaSedEO9pHfSikipDNco3Y3ClqUcOdn85UNZIV0kXc//ks\nzn3ljF6fCF14Dz3dj+VrAagFDQIC9jYbspEcbv/4PoQAHO02KBYFJpuCzol2WD0WxJeT4BqHu9sJ\nxaJAkhjahvW486kiHB12dE60wWw3IRfNV8odJLlcuvHiMBhjKOVKCN4NIxVIw+axQDLLugXQpu/6\n8HQU0fk4Bp7sxcr1NRTTehMRk/RGwJHnBuHpqa6Ts3msePjXJ7G4GsI3X07gpeRNLP27o1uXJZsU\nuPt2tiVOHH8Ui7LrdaReVo9r4sRm/JqN1WXBw9+YQmQujmwsB5vbgvZRX83vIhXKYPHKCnIJPcvq\n6XNh6FI/FIuM+7+YQzqcrXxPrN1aR2Q+jvOvTFTtbh0Wjg47THZFb7zctG5KMtvVNFNif5jstro1\n+oxh377vRxESwScEtaBi8cpq7RWc0EsJrv/7W5Vso9AEei/60XO+q6kx5BN5qEUNNq8VskmGzWvd\n9jFaSUMhVYTJbqrxBo7MxWrqTAHdhWHt9jqGLz2oEW0f9qFtyAutxBH4NIjQ3UhV5jYTzcHTq38J\nJFZTuPM3M5VmLqEJ9F30Q7EoWPxgpZKpKmaKSCwlcOYLozj75XGkghlkIlmY7SZ4BzyQFQmFVAG3\nfjwNrvIHzyfpi/dmH2SuCXBNw9zbS1WvRXBALWiYeWMe5796BhZX9SKzlovD7DPhoUdk4PdnAAxv\n+54SxFHF3mZDNpar2SmRFAn2NqoJ3imySa4Z/rOZbCyH+xs2atB35OLLSeTi0xh4rAeZSK5qSJDg\nAqVcCev3I+hu8vfCTmCMYfKlUdx/Yx6FpJ6N5Fyg80w7uibrv06iucgmBW3jg4hOL0FsaoRjsoz2\nyRFI8snb3SMRfEKYfnO+boPYxmK3edELfBKEvc1Wk33cC4V0EdNvzKOQKgDlMoyeC7rIFkJALWiQ\nFamqxlUIgeUPAwjdi+jbL5qAu9eF0WcHKhZpuVjOeOtUADmD5juuCUy/MYd0qNaiTGgCidUUMpEs\nZt6crznvysdrAGPV0+PK1kML76/g/FfPwN3trJlytHQtUGt5xnWhvhs4Fwjdi2Dg8d7KbZubJsY/\nuH4kmiZ2i+AchWQaTJZ1dweyNDvVdJ/rRHQ+rpcmbcB0Edw27G1dYC2Gc4FMOAsIAUeHfd8Nboae\n6kKfzBm6H61+/zcOawKxxURLRHA2lkMqmIF/qgNWjwVcFbD7rAe6Y0kY478wBUlWELk3B8EFmCyh\nY2oUHVOjrQ7tQKC/sBNAPlVAJtLAkcGAjels+xXBggvc/ZsZFHMlPbtTXngDnwRRzJQQX0zotmEA\nfIMeDF3qg2ySEbgRwvq9CIQmKsIzuZrCzFsLOPOS/mGzeaxgMqsZawwGwyzz8oeBhh69TGII3YsY\nOkAIDoAZC9dcIg+tpEFSJOTierOhzWsFYwyJQKrR27NzBJBPPNgOPopdw7slsbyGwNUb5S5jAUmR\n4RnqRfuZYZgdlPU7jVjdFpx5aQTz7y3rW98QcHQ6MPL0wIGNcD/qJFZTmH17sbINzaDX/RoNDNop\n9Rx6uMrBS1rdGUGHXQohuMDsO4uILycBoZeHQQBjLwyTAG4RjDF0PTSBznNj0EoqZJOxnelJgf7K\nTgDFdBGSxKDtckhFKbf/Ts/kWtrQG5drAuv3qgclxBYTKGZLmHx5FEGDBhnBBVKhDArpIixOM9pH\nfVj9JFjzuiSJwb+lTkwIgchMFKjvUAbBBcTmsoWaOxjfzKDX1y28twytpD+BbJIw8uzeJ6EZYbJX\nfxw3BPBRaZrYQAiB5FIA4btzUPNF2Dt86Do/Dov7QZY8F01g5f2Pq8pZtCJH9P4CojOL6Hn03L4m\nyRHHF2enAw99bRJqQQVjrCU1qEeFQrpouDM19/YirK9MwObZvqTMCLPTbOgnLCkSPH0uZKK5muSC\npEjonKguPSjlVcSXkxBcwNPrgsXZ3Abr9fsRJJaTD3Yry8vFzJvzuPDquZY16h00G0mOXXlyHjJM\nkqBYjqbbSDM5nZfeR4RsNIfljwJYurbaMIO5HVaPte6UNiYx49+ypLsn7JdCurhjs3vBBbKRLNLh\nLDSD7ThAF7gbi7diUTD5xTFY3RYwmUGSGUw2BWMvDNd+OZRLF+rC9C/fRkab9QZmCAHMvPHAYYKr\nHKWciuk35mDzbNMoUD4lk5h+/gYX1JG5OFLBo+f8sJXgJ3ex8sEN5GNJqLk8kssBzL7+LvLxB6b2\n4TuzhvXcAAAusPbRbZSyu9u9IE4WikU51QIYgL4bZrB+ci4Qurv3aYs95zvrWFUydE11ov/R7oqr\nBJi+9rUNe+EdeODHG56J4pMf3MbS1VUsXVvFjf/3LpY/DOw5JiNCdyPG6zbTx8ufRGrK3P7o6Arh\n0wBlgpsM1ziEwLZbe0tXV7F+/8ECsH4vgrYRL4Yu9e+6btJsN8E35EFsMVF1dS/JDANP9CJ4J4xC\nqliVAZVlqabrVgiBZCCN2EIcTGJoH/XpwrEBdp9eFiC2G5O2AWPIxfJQzDLUQq3TAddExfVBP78N\nD319UhfbGofFbTF8f5jEYPVakY8beyO7u53oPt+JhSsrdUNz9ziRDmZ0gb61nM6whELA7DQjGzV+\nTovbDFeXA/lkEc4OOzon2xG8HUboTtjw/kITWLy6ivNfOVP1vHdyNswUrfDKKh63p2GVWrdolnJ5\nRO8vVPtICn3K0NrHdzD8wlMAgEJyezGfXF5D+5mRgwqVII48+VTB2I5KQO+x2ALXOJIBfahEI5cf\nd48LA0/0YulaoFL6oFhkjH9uGLIiwT/VCW9/+TuDC3j6XFVDfAqpAhaurOjlapsWw9C9MFx+R8Vj\nfb9oJWO3G8FRKaPbD5USkyPSi3ASytxOGiSCm0Qpr2Lh/WUkVlIQQsDmsWLwqT7DEcCpUKZKAAO6\n+IvOJ+Ab8OxpgRl+egAmq4L1+1EILiCbZfRd9KNzoh2+IS+WPwogOh/XG9B6nBh4vBfmTd7BggtM\nvzmPVDBTaZqIzMbQMd6GwSfrj6d1dNhh81iQjeV3NLmNqxyLV1YgmaSaiWgbE9WMLNV2sg03+EQv\npn85Vz1RTgY8vW5k1rOYfmPBsCFkA1mRcP6rZ/DJX97Z9rkAVKzo7O36BK3NwpnJDIOP99b8Lgef\n6IWj3YaF95YNMyC5Te9jPi/w928+hdmMGyXBYGIcfxrpwu90r2DM0prxqNlwrK6PZHY9Vvm31edG\nIZWuW2IiBG/4uyCI04Cz04HESrLm88RkBkdndd18YjWFmV8t6J8ppq/ZfRf8dRvZOifa0T7iQyaa\ng2ySKn0MG1ic5rr2Y+HZmHGGWhUI3o00TQS7e1yIzMZqbmcMNU3IuyGf1EX8xs6ap9eFoaf6qr7z\nWsVr34pj/ErrG51L2TzWb88gHViHVHaF8I0OHJkLhsOCRHATEFzgzk+n9W388rqRi+dx/+ezOPvl\niZomrshM1FAAcZUjPB3d0wIjSQwDj/ei/9EeaCqHbJIqf8yKWcbwpf4qS7GtxBYTVQIY0IV5eDqK\ntmFv3YwwYwwTL41i8cqynlUQemba0+9GZNr4dQIAL/HyCOWybRuA9hEfBp/sNbz/TnB3O3HmC6NY\n/mgNuVgOilVB50QbVj4O1jbXGZBPFpBYTVXcKnaC2WXG6LODWLiyjNiiXg6gWGQMGAjgDawuS7ks\no/Y5NpdM/Nn3ipjOuFAS+q5CQehbx/802It/OjCLRsPsDgrZVH/J2Dwco2NqFMnlYJXNzmaYJMHZ\nQ/6fxOlGtsiGF5RMYug601H5uZQrGdYOr5Zdftx1GpwlRTJMxGyHWtDq9lc0c7Jf7wU/4kuJSq8F\noH8nuHc4Xt6IUq6E2z+ZrnLtSaykcOvH03j41ycr7kOnmVI2j5m/eRtaSa1sc65dv41MKIKBpx9t\ncXSHC4ngJhBfSepNZlubw7jA6qdBjD0/VH17A4FVr1Z2pzCJ7amZIDxjbJujZ6jjDcsi8skCUqEs\nwBgY07exXF0OmKwK1m6EAMaMs34CMDlMmHxpFLJFaUp3uLPTgakvjlV+Xv00WDcbuZVsNI+la4Ed\nC2AAiM3FMfBoD0afGwJXOTSVQ7HIDa+m7e02KBYZxS3vyUYJylouDkDgrZ+pKIna36UqGO7k7Thv\n23sd+V5xdLWDSRKAanHLJAnekQcXWVaPC0OffQIrVz5BKVNd+8tkGe5+P2y+5mSTCOI4onu7G5dn\ndY63VfmmR+ZihiVZXBMI3gnXFcF7xdPjRGQ2VrNuM4nB29e857I4zTj3lTNY/SSI5GoKkllC15mO\nhh7I2xG6FzH+LitpCM/G4J/sMHjUwbMxJvkoNMOt35quEsCAPho5tRpCLpY8VWszieAmkIvl64q8\nrIF1mW/Qo08rM3hMPlWEVtKacrWaS+SxfC2A5FoaTGZoH/Gh/5Fuw2aURqUMjTxvS3kV916frXot\nQhOY/dUimAy0j7TBZFOwdmvdUFwW06WGW1RCCGSjORRSBWglDtkkw93jrKmF01SOyGwM8eUEZJOM\nzjPtcPudyCcLOyrTqLzWXV6EqAUNscUE2kd8kLZ4IdeDMYaJF0dw92cz4JrQvRglBpvXCtNZGzZq\nxv7eX5b3PQ3I8db0tDJJwuBzj2PhrasARGWCntXrRtf5iar7OjrbMPHKCyikMojPLSO9Fta33cYG\n4RnsaUn8BHFUiC0l6/ZTxJeSVZ7hxaxadx0rZktNj83T54bVY0Eunn+wbkv6LldXk0WkxWnGyDMD\nTTtfOpQxfK+4JpAOZVoigoO5JLhQcekLSUwqbsR/PIdWDj5KBdYNxbgQAplQhEQwsTvMDhMkRTIU\nUGZnbX2rt98Ne5vVcKhDKVvC8kdrGHqqfh3uTiiki7j9k2m97AC6yA1PR5EOZXDulYka37/2ER8y\n4WxNllpSJLQN1Texj8xE67pDCE2vK7a4zXUX+61T4jaTTxVw/xdzKGZKDxY1pv839GRfxc5HLWq4\n/ZNpFNMPmkxiCwlYXGa0jXiNvYabSOheBO0jPgD6+8w1DkmRGmaDbV4rLvzmWSRWUyhlS7C32ZC0\nF8AYKl3D46Ze3CnUZuA1wXDG2jpnBXuHD2e+9iJSK0Go+QJs7V7YO3zGDYuMwep2ovviFHCxBcES\nxBGFN7Br5FucVVxdDoSna3frmAS49lE7Ww8mMUy+PIa1myFEZmMQXMA74EHvw11H3r/X4jIjFcrU\n7AAyST922GwWwL+7fg2fvibQ6smfskmBavAVwpgESTnav99mQxZpTcA35DU0k5ZkZjiamEkMPQ/7\nDR8juDBsFNgtgZuhmgVTcIFCuojEau2Ah7YRL2w+W5WtjqRI8PS6Glqp5VOFhgJTcIFCqgjZVPun\ntrEo3f/lHFY/WUMpr9eaCaELyXs/m61xtYAAwIHFq6v6+FUAa7dCKBh0WRdSRYTuRPTXtI/62fbR\nxpOscvE8NJVj/v1lfPj9G/joz27i0x/cQXQh3vBxkizBN+BB12QHnJ0OMMYedA3/kcBvmOZh5iWw\nTS/Mwji+5InBLe+/c3o/yCYF3uE+dEyNwtHZduqaKY47jLF/xRgLMcZutDqW04qnx2m8LjHA01+d\nifP0u2F21A4tkGQJ3WcPprZeViT0XezGhd84i4uvnsPQU30w2WqTOkeNrskOSAbfrYwxdI4f/ghm\nLjS89q04fnf92pGxQ/OND4IZTiUUcPf7Dz2eVnK6JP8BISsSJl8exfQb83pDQbnnaeCxnrq1WkLl\neobScNtm73XBakFFZC6G2HzcsBaWqxzp9Qy85UWWa3ozXnhG7wb2DrhRyqmQFAkdY23wDrgb17e2\n2SEpcXC1gRDWhOFgDiFQuWJPrqWxdjsMR4cd6bW0vlPTQFeJ8pjh4Uv9iM4Zv1ZArwPzn+1EJpp7\n4MG7y3UoMtdYzPISx92fTSMXf3BBUMyWMPfuEiRZqrzXuyE2vwL12g38bfM1XO69hBVXLxylLL7e\nncVnvM3f/iROHX8M4P8E8CctjuPUYvVY0THqQ2TuwRhpJjHIJgm9D1cLEUlimPq1cd3lZy4OIYRu\ng/ZYj6GbzmnG7rNh+OkBzL+3/MCnHcDo80NNH/ZxXGkbHUQmGEF6LQzBeeXiqu/SxVMxIGMzTRHB\njLF/BeCrAEJCiIeacc7jht1nw8PfmEIupmcFHW22hvWhzi5H3Qyqs6N2pKzgAul13b3B2ekwrOvN\nRLK4+/oswEX94Rkyqyyaggvc/dkssrEH04MKyQKsHismPj+yo/n17SNerH4SBNdqGwO3ZXOCtzw+\nORVIGx43emxpoxaugUgXQq+NnvzCKLjKoZY0fPIXt3cX6w7um43UWpYJTWD5+tquRbBa4ghcuwmh\ncbTnYvjqzE/0AwxwxrrAnnt8V+cjiK0IId5ijA23Oo5mwrnedLSTdeuoMPhUH1x+J0J3w1ALGjz9\nbnSf7TDMuO7E5YfQ2Rj8kV7PgjHA0ekwzA6fVpjEMPjsY8hFE0gHw5BNJrgHuk+dAAaalwn+Y1BW\nAYwx2Nt2ZuuiWBR0n++sGR8sKfqAi82k1zOYfqNsj7PhD/lId9U2mBACM28tVGqAG8XYNqxv78eW\nEsjFq8dnck0gn8gjtpBA+6hv29chm2Sc/dI45i8vGdZhHRSSzODucenDSXiD18weeAxLigSzIsHu\nK/v6HgKFZK3h/XbEVrLlGuotCCAdWG9KXASxHYyx3wLwWwDg9B3dLdKKR/tyEgJ6QmLoqT44DJIJ\nR42N9XhjTSaahyRL+/IaPg3Y2jywtXlaHUZLaYoIPolZhcOg72I37D4b1m6GUMypcHbY0XvBX+Ur\nrBY13Pv5XE197+r1Ndi91kq5RS6eN5zAtoHeqAWMvTBcaWyILSYMyxi4JrD4wQpiSwnYfTY4Ouxw\ndzsNa5gBvcTC2++Gd8CNXKKAyExM97sVu3db2CmyRUHHmA+RuRhK2fq+lZLE0LnFbmfgiR7c//lc\nQ6u6ZtGo8a8uDRIWVHtLHBZCiO8C+C4AdA5OHY1ixi1wLvSm2MwDj/ZsNIe7r8/i3CsTVdMnDwMh\nBMIzUYTu6JldV7cTvRf8ujc4UYVW0gDGmmKNedIpZXOI3JtHJhyD2WFHx+QwbG104dQMDq0m+Lhk\nFQ4b36AHvsH6V2LR+bihlQnXBNZurVdEsOCioXgafrof3n531VZho8VHK3HEl5KILyXBZAbFomDi\npRFIEoPJokA2y5Xsc2JFb7RjEoMQAoNP9UIrcoTuhmu8cPcLkxjaR7zoe7QHkiJh+aO1+vdlwPAz\nA7B59IsKrnIsXFnR31Po75dikWFxmmF1WRBbMrat2yuSzNB9fmdNKxsekpOKG5E+O+ql1F19xtOh\njhK5WBLR6QWUsnk4/e3wjQ5ANlPdItF84ksJqHkDj3aNI3Aj1FTrrZ0w/94yYvPxygV2dC6O+HIS\n5758+IL8qJKN5TB/ebnS2OzsdGD46f6WXCgILpAIpJCL5WF2mOAb9BxAOc3+vIHziRTmfv4eONcA\nLpCPJpBaDaL38YfgHd6fixRxiCL4OGQVjiLFbLFuxrKYedAgZfPZ6mYJbV6roc1Z+1gbopsW7HoI\nTaCULeHWj+5BUvSRue5eF7KRbFXD20aT3+KVVb1sY5vzbojmnZZQSIqEoUt9FTuyVChTNRWo5vWN\n+qpe9+w7i0ispqqaEbWCBu9ZD/yTHUis3gY3SCrLFll/np3EyfQ4BRfomuqoyUIbsWGh89o3Y9Au\nX8dqeBR9lxxYuvwRsKVxMh9PoZjJwezY2zSlgyY2t4zAhzf1EhUBZMNRhO/OYezlZ2CyH82YieNL\nNpqr69GeCR/uMJl8slAZTb8ZXuJYub6Gsc8O1Xnk6aGYKeLOT2eqfmfpUAZ3fjKNh359ak+DnvZK\nKa/i7t/MoJgtgau6reXS1VVMvjxWM+V1LzTLGzhw7Sa4Wv3FJDSOwIc34e7vrprUSewe2oc44jja\n7cYNdgxwbpotL0kMQ5f6qizONgTZ0CXjq0VXlwOdk+166cIO4ao+3CGxnDR0fAB0MdxQADOg77Fu\nPPT1STjad1G3x1DlURlfTjQUpu1jbZV/FzO6NdzWuIQAVj5aw/W/uPWgnITpDYQbmdyLv3l2R2UI\nbcMePPS1SUy8OIKLr56Db9CDmbcW8Olf3sH9X8zpNdNbCGRjFQE8/sH1ioWOu8+Pzqmxmqa/YjqD\n+Teu1PVm3g9aSUU6GEY2Et/T+bWSqgtgjVd+L0Lj0ApFrF2/0+Roif3AGPu3AC4DmGSMLTPG/stW\nx7QXLA5z9Zq3+dghOwFU3GcMSK7VP3aaCN6NGDsiqRyR2eihxrJwZQX5dKEiyLnKoRY0TL8xv+/1\ndbMA/va4A/HvfA9La8O7Po/gHNlIPctUhly0sXMRsT1kkXbE8fa5YbabUEhX++BKsoTuh6q3xtuG\nvLA4zVi7tY58sgBHux3d5zsbbjMNPNaL9hFfuY4tclAvo4Kjw4bxF4Yr3c9Wj2XHGRvGWJUxvKzI\nFTu6rShWpeoiIZ8sjpLOAwAAIABJREFUQJIYtDrinBc3vblCLxV56OuTUCwKMuHstlPn/Gc70P9Y\nT3k4hAWJ1RRm3pyvZNkL6SJSwTSGnu5H+/DmhkNRI4A3iM8v126jCUArFJANx+DobEOzCN+dQ+jG\nPX0kshCQTAoGn3scNt/OmyYyoQgYkyBQm5lLBUJNi5XYP0KIv9XqGJqBb9iLpY8CwNYhP7soRWoW\nslmuOxTIyCf9NFJvLeWaQCZ8eAOAuMaRWE7CYKlCKV9CLp6H3bf3nSsuNFx6OYXfDe13OEZ5OpRh\ntkeUR9gT+6Ep7+BJySocRZjEMPVrY1UDOZyddkx9ccxQ3Dra7Rh7fgjnv3IGw5/ZWZ2V3WfD4BN9\nsPn2vwXUCCYzeAc8VfY/GYOx0lWPkRiYzMDKDW689KD8oW3EeEgJkxj6H+uuyt5aXBZou/BfVosa\nwnP6FXjgZn0B5+lz4ZH/5DwGHu+tPJ8QAgvvL9eUmXBNYPHKas2XwJTJg9SP5/THcgG1UITgHGq+\naPicgnMU083b6k0FQgjduA+hcfCSCq5qUHMFzL9xpWYbrhH6y6dKJ+LwUMwyJl8ahcmm6GPLTfro\n8oEneuHyH64zgKfP2AqRyawy3fKgEUIgFcogvpSoDB86SljdFsPeFSYxWN2Hl7kXXNTP9jIGbRuX\npZ3w6vD+BxoxicHV01nnPZOoOa4JNMsd4kRkFZpJMVtC8PY6UmtpmBxm+Kc69mzXolgUjD47CPGM\n/qE9KIeA0WcHceen07pYPABTB8YYOjaVKGglDfl4rb/uBp2TbcjFCsiEMwBjCN0JI3h7HcPlbKrV\nZUHfRT9WrgcrwlKSGTz97krd8AYWpxmODgcyBiUJhgggeCuMrjMdDbc5rW5LTR1bKafW/QISXCCf\nLNTUnAkhELo5jcjdubJ5uQTJbIKWr7VYE1xg/dY03H3+pjSchW/PQmgGC7YQSC4Hd9x84ehqN+7/\nYAzuPmqGJQ4GR4cdF37zbKU+uG4J2QEjKxLGPzeM6TfmAehlY4wxOP1O+M81NysdW0pg+aM1FFIF\nmCwK/Oc74fI7MP3LeV3AlXsy/FMd6Hu0+8i4yvjPdiA6F6tJEDCJoeMQp7nJJhlWlwV5IwtLgR1b\nnR4GPY+fR+71JHipBK5qevaXMQw882hdxyZi51A5xAGQTxVw+8fT4KqmlzDE8kgFUui96Ef3ub13\n9x/0QmbzWvHwN6YQnokhEUihkCxALWqQZAatWH/OfSOYzMAAKDYFY88NVVmGFVLGmc4NSlkV2Wiu\nXAYiKtuMc28vYf7dZVjcZr0zfPPbwvTtrDs/nYHL70DXZEdlOMjE54bw8Z/f3vHrKOVKmHtnsW5W\nQCp7DtfcXraHM0IIAUmRqmrGhHDg/nvrCN+J6fW00LO9aOB/rOby/z97bxbc2JmmZz7/OQc7QBAE\nwX1fkpmpVKak1FIqlaRapOqqrl7sbseML2bmyu7pmCXCt7bDfTFXjp6YNWIcYcfEtH3jcIfdbne5\nXd3VVVKXSiWVtkwtuTHJ5L6A2Pcd5/xzcUgkQQAkyCRTVApPRC0JgAcHJPDjPd//fe9L6PYCQ889\n1fJ58pE4mWAERVXxjg9h8zSPv67km1+IGFWd4Kd3AdoSwoqmMfzC02x9/IX5O5YSoaqoVgv91y4d\n+fMdOpwUIcTx5gvOiK4BN9d+7xKJjZRpkdbnOnW/4vhqkpVfb9TmGyrFKlufBkGIhpmH8P0ojm57\nW57vjwOH187Uq+N1569aFKZeHX/syXfjLw2z+Ha9VaaiCkaeHXgk27ZgPsGjOkLsx+KwM/vD10ht\nBCnEklhcTronhrE4Om4jp0FHBJ8BGzeCDa4Fhi7Z+jxE73RP3XDXeUOzaQxcDjBwuT6IY/tWmNDd\nCEiJYUgsTgvVYvXQATiLU2P225MI1eyTPSjiLU5L63YnzD7aVpZl0pAUk41X8UZVkg2ZrQL5eIHI\nQoxLu/ZEmlVj+rVxlt5dO9K5AszqRGK99fCdogp8Y14M3cCoGlRKVeLLSSqlKjaP1awyHPhZu8dG\nUi3WDU3E/smfsHIzWRPAD1+MYfYYNFlMpSFJrwcbRLA0JNlwlPAX9yllcuYxhSB6f5m+KxfonZts\nOJbD76WSb96WYlSqbN+4g6Hr9EyPtf5l7eIdG8Tu6yKxtE45X8DV56d7fBjVcn7f8x06nCaqVa3b\n8TpNpJRs3Aw2DvjuFgoOYuiS4J3wuRHBAN0jXTzz+5fJJwoIIXD47F9KpdrT7+bib8ywfStEPlbA\n6rYyeKUP75DnxMfcE8B//A8S9P8vf87dEwzDNUPRVHyTI/gmO2mBp03nm+kMSAczTW8XiiATyh3q\nC3weEUIwfLUfAWzfDpmRxfvs2Q6iaAKLw8LsdycP7UnWKzqaVW0a8mFxWh65vVQaEt2QrH+yxYXv\nTgHmAjz3xhQbN7YppsuHWqwdJZRnvjPJxqdBYg/iu5GtD+/bc9xQNKVmv6OogunXxknIXE0A3/qD\ndyhl+hHiQdOXKwSgKI0CGRqS8koZ0zlCL5Xqq93SdOsI31qga7gfq7u+MhW4PENmO9K8JQKQuk74\n1gK+ydG2tt9sHhcDz3Qqvx06nDZG1aBSaL32NqN6DnuDhXI+KvfOHnNQ+zTYE8B/8Yc6yT/68xO5\nQXR4/HRE8BnQakIYyVe2h6eYKpoDYkf0CgtFMHApgHeki8xOlmKqhHfI0/C6y7ky936y2LLVoJKv\nYE6/UueKcRLS21k+/7O7VMs6Dq+dUraMNCTGMQblDqKogtDdCMmtdFOxLHcjrrsG3Th9Dmwe60Mj\n9nyOfzrrJvHP/jUwgWa3tmzRkIdkoHiG+vY9TrL2y4+pFlr3WEspSW0ECVyarrvd7vUw8e0X2Pr4\nFuV0855pQ9eplkpYHKczPCmlJB9NUMkXsHu7sHefvPrSocPXBUVVUBRxrLTL8yA2vy788T9Mov/6\n044A/grREcFnQM9kN7GlRFNh0zV4vrPMpZRNt6bia6k2e2kl8bUUO3cjSMwLAkVTmHtzqpbcBhC8\nE0E/Ip2tFod8SMtEu+x5Gufjp2PD4wo4SW6mD/+d7Br2z7w+gS0Rp/e9v0XoBpnpceDhdqlqseAd\nGyS1Hqyr7gpFoWt0ALvXQ/jOg1qlVigKiqbSf3Wu9thCPIleOrzHGikxqs2rvU6/j4nXX2ThL3/R\nvJdNmud5GlTyBVZ/8RHV3aE/KSUOfzfj37qOonWWpA4dWiEUgX+6h+hSvOHiW9EUpKz3aFdUwdC1\nRxtKldIMSxKqcrIY+HOCNCTpnSzVYhVXr7OT4NcB6IjgM2Hk2UGyoVwtiUYoAgRMvzp2BpGMj440\nJNu3QoTno+gVA5vHysizA/jGuuse044QlQa1SiuY42xG1WDxrRWe/rsXawI7Hcy0dTyhCtwBJ/l4\n8dDWhceKAHevk1yscOSFgTQkw7/4GVM//rNdMS+ZAD7Tv8X+/KjB609h6DqZrTBCNdsfPMN9DF2/\ngqKpOPzdxBZWqRZLuPt76ZkdR7M9tBQyBfDhuwxCVeuqxwDZUJT44hrVUhn3QABnbzf5WLIuqW5P\njJ9WMtH6ezcp5wp1YrsQTRL89B7DLzx9Ks/RocOTiDQkg1cClHNl0jvZ2npqc1uYfn2C8P0o0aUE\nRsXA1etg9PrQI1WC0ztZVj/YNFswJDh8dqZeGfvKCchCssj9ny/XZkyklHSPdDH1ythXdne2w+nQ\nEcFngGZVufxbF0hupMiEclidFvxTvsc+/douqx9sklh7GJ9cypRZeW8DhMA3ag5+aXbVjDk+RPTt\ntS40e0y1rJOL5nEHXBiGpJQ7omq5i9QlhWRrAXxY9PJptFI0Q1EFVpe1reG6CU+Rqf/8Z6jV+j6+\nhX/5Pu7fGADL3jFVRl9+lmqxRDmbx+Jy1k3/ugI9h4Zj2H3dDT3CdQiBZrOSjyWxeVyoVgvhOw+I\nzj+0Rysm0igWDbvXQymdM9t6pIEr0MPQ9eYuFMellMlRSmcbqs3SMEitbTN0/amOAXyHDgeQhmT7\nixCh+SjSkCiqoO+CH1evE5vbirPHgRCCseeHGXu+PUvDoyikijz423r3hHyswPxPH/D037mIajmf\ncb2GbpAN55AS3H0uFEWw8NZyQ290cjNN8E6Yoac79o1fZzoi+IxQFEHPeDc94+fbzLqcr5h59weE\nq6FLVn+9QWg+SjacM2NVDmg+oZqtDjaXZdf/ttz4oNqDqQ3AxdeSx/IhrhbrBbBiUbB3Weke8dJ/\nOYCQEHkQI7Zsxv1anRY0m4ZmU4ksxls6TBwLYYpfQ5dIAzZvBo9uD1Hg2fQ8SpPACT1fZv2LBL3X\nd19jqUxmO4zUDdyDgWPb31gcNronR0iubjUdcBPCbEMI31ogevcBo688R/TeUp1wloaBXq7gGepj\n+MWrlLN5bF3ultZqerlCeiuEXirjDPTg6PEeOeVdLZYQrQb9dts1VGtHBHfosJ+Nm0Gii7GaINUN\nSWQhhmpVz+w7ZuduxBz4PYChG8RXk48tAOQ4pLYzLL+79vBbyJD0XvA3nT2RuiR8P9YRwV9zOiL4\na04+UTB9gJssdnrZIBvaHZQ6oKssTgv23WGv7pEubv34/qGi0KgauHqdZCM5Vt/feLSTljDyzCCK\nRaVaqGJzW+m/GKD/Yr0hvaEbhO8/WhS0UAXD1/qR0hzWiz6IY+iy6e/rIBa7hi1XQLTwi6zsXhQk\nV7fY/uT2w6r2Z/fouTDBwL6e33YYfO4yNo+L6Pxyrd92j1p7iq6j6zpbH32xF/FWj5RktkIMv/A0\ndm/rYbVsKMr6r27uHtsM93AFfIx96/qhlVx7t6epAAZTyCsdK7UOHerQKzqRxVjDzpOhS3buRBi4\nHDiTNrtCvNC0pmFUJflE6wHcL4tyvlIXVb9HZD7asuVBr5xOi91pewN3eHx0vnG+5lidlhOFYFQK\nFSr5CrlYns1Pg239TCFVZOmdtUcecjN0g4W3V8xBEEPi6nEw/dp4XRwzQHwlyaM8mWJRmHx5tGZp\nd++vFo81lW1zWYlNXqP385to5XpRqmgCa+845Wye7Ru3kYZR17oRX1zDFegxIzMPIA2DfDSBUdVx\n9vpqqXFCCLonR4jOLx85TFjJF6HFF8NRPXJGtcrGezfrKs5S18lF4kTvrzS4T+xHtVjwz00SW1it\n+3mhKvRfu3hukq06dDgvlLLlQxyHJJViFZvLimFIdm6HCC/EMSo6rl4nI88Nnrgn2N5tJ58sNqwj\niqbg8J6/nuDoUrz5TK9s3qIH4DqFZLj93sAzH3/G3T/pCOGvEp19x685pn1X8zz3Q9n9nBtViVGV\nR/fHSlj42fLpDLdJ8z9GxUDqkmw0z8LbKw1Z8OH70UfqCXb6HHSPdtX+Xc4fw59TQN+lXqJXnyUd\nCFDd53ogFIFqc+y2L2w2XaClrhN/sNZwez6a4P6P32b9VzfY/OBz5v/iLTY/vkUxlaEQT7H8s/fM\nKvARfw7RIoRDKAre8cN7CjPbkaa3S90gsbR++BMDfVdmGXj2EprTDkJg7XIx+s3n8I4OHvmzHTp8\n3TisUCGhFr609MtVgnciVItVDF2SCeW4/zdL5BMnc8QZuBxoeVF6MJb+PLB/IPsgmlWtebfvIVTB\nyHOPtuZ0BPBXn04luAOz351k8e0VSpkSCFPYfqWQUEgU+ezf38XeZcM37iUw3XOkBdtR5OMFkusp\nUtsZkltpqu0KeAW8Qx58o16kELz9h/8z393+a8Z++gV6JI1reJTei1OoFo1qqdJyC616wPJML1dY\n++XHDTZnqZVNUiubx3ptFqed3sszBG/crkUcIzAH6Bx2jKre0g1Cr1Ra7vq1smDbjzQMcqEoerGM\noqlUsgWSK5u4Aj2n5kDRocOTgmbT6B7parBkFKqgd8qHqinkEwUywWzTlomtT3eY/W5jUuRRWBwW\nM7DywO1SSqoVHdX65X1WpSEpFypoFrV2Hp4+F4m1VMMMiGkr58Ni0wjNR9HLOk6/k5FnB3AHms87\nHIc//odJZj7qCOCvKh0R/IRRzpUJL8TIxwrYu230z/Wald59SEMSWYwReWAOjfnGvMy9OUU5X6GU\nLbP6681jV2zNLXR5Jm4M7aLvOlDkonk2bwZx97koZcsn7ogwqgbL762bgq/dYwiYfHmEnglfrYqi\nW60k/8Fr/I///Ack/tm/rjNSdw/0klrbahCPQlHqWiGklMSX1psOqhwLIRCKwtALT5uOE70+Nn79\nKcVE2mxp202HS65sMPW9l5v69rr6/C2Fu6vv6GGZ4I07ZLbCuy0g5hsmEwyzfeM2Iy9de7TX16HD\nE8jEN0dZeW+d1FYGoQqkLvGNeRl9fggw/chbrQzZaP5Ezxldijc/ppSE56OMXh860XEflZ07Yba/\nCGEYEgF4R7qYeHmUnvFutj8PUdaNuvVaUQX9FwNYnRYGnupredwOX086IvgJIhfLc/9ny0hDmsbg\noSzRxTgz35mka8AM6ZBSsviLVTKhh1WD0L0o8ZUkl380i9NnWu0sv7t2rP5XRVOY+fY493++fCzn\nhzNDYg71PUqL6V7HwDF159anIbxDXbVtyj0St7a59bNt4uEgTn83/gsTeAYDWD0uSqnsQ6cGIVCt\nFnpmTCfhXDjG1se3zD7ekw5eCIHN48IZ8OG/MFlzfDCqVdOybB9S1yln88QW1wlcmmo4lM3jwjs+\nZIZ77OvrVSwafU9fOPQ09Eq1IRTEfE6D9MYO+rOXaz3OHTp0MFE1hZnXJyjnK5RzZWweW11whWbX\nWvYNayes2ObjheZpmMbphQ4dl40b24TuRR+eC5DcSLNYXOHSb8xw6YczrH+yTXLDrJq7+12Mvzh8\nbu1JO3z5dHqCnyBW3t/AqBoPt8ykuR228t56rV82G86ZHor7FjdpmMMVoXlzceke6eLSD2dx9bY3\nNCAUwfhLw6gWFeWcebyK2n+dgBMIYCRUitUGVwrnW3f52Zv/gq35FIVYktiDNR789buU0lkmv/MN\n/Bcn0Rx2NLsV39QIU2++gmazUkpnWXv3BpUD4RLHQlEYfvFpZn7wKkPXr2DzuMjHkux8ds8cymtm\nV6YbpNa3Wx5y6PkrphuF14PmsOOdGGb6zVda2qntUS2WDh3IO+hq0aFDh4dYnRbcAVdDcluzaHrY\nrYJe6j3Rczm67c2HZIUZmvG40St6nQDeTy6aJ58oYHFYGH9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UKqdCQBWyvVyGxm8Q8Y4r2cq6BVjydKpS7JRnPkonmEgMm3xhi5bPS3GRUDYxDNH3YSHB8yhgFO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\nmE1oGR+CozN/20hwZgGLV5+2JE6GIgjNLGDotWdh3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in6mNFuzdWKzv9DgNG2fZKajiUw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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WijzY-vDNbE9",
        "colab_type": "text"
      },
      "source": [
        "So far we looked at accuracy as the metric that determines our mode's level of performance. But we have several other options when it comes to  evaluation metrics.\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/practicalAI/images/master/images/05_Logistic_Regression/metrics.png\" width=\"350\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80MwyE0yOr-k",
        "colab_type": "text"
      },
      "source": [
        "The metric we choose really depends on the situation.\n",
        "positive - true, 1, tumor, issue, etc., negative - false, 0, not tumor, not issue, etc.\n",
        "\n",
        "$\\text{accuracy} = \\frac{TP+TN}{TP+TN+FP+FN}$ \n",
        "\n",
        "$\\text{recall} = \\frac{TP}{TP+FN}$ → (how many of the actual issues did I catch)\n",
        "\n",
        "$\\text{precision} = \\frac{TP}{TP+FP}$ → (out of all the things I said were issues, how many were actually issues)\n",
        "\n",
        "$F_1 = 2 * \\frac{\\text{precision }  *  \\text{ recall}}{\\text{precision } + \\text{ recall}}$\n",
        "\n",
        "where: \n",
        "* TP: # of samples predicted to be positive and were actually positive\n",
        "* TN: # of samples predicted to be negative and were actually negative\n",
        "* FP: # of samples predicted to be positive but were actually negative\n",
        "* FN: # of samples predicted to be negative but were actually positive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N8jPq_Cy2hp",
        "colab_type": "code",
        "outputId": "f41499c4-c890-4ea0-a1b9-68dce0f26b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "# Classification report\n",
        "plot_confusion_matrix(y_true=y_test, y_pred=pred_test, classes=classes)\n",
        "print (classification_report(y_test, pred_test))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UZkB6ks1cU+AVkZSXZHFXgVdEUp1hSbavgwKviKQ89XhFRBIoeJ0suSKvAq+I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\nJJgCr4hIgv0/nXKj3OUNtbwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      1.00      0.97        58\n",
            "           1       1.00      0.96      0.98        92\n",
            "\n",
            "    accuracy                           0.97       150\n",
            "   macro avg       0.97      0.98      0.97       150\n",
            "weighted avg       0.98      0.97      0.97       150\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9v6zc1_1PWnz",
        "colab_type": "text"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX9428-EPUzx",
        "colab_type": "code",
        "outputId": "48154a48-c8c8-42a7-97de-fdc7128552df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        }
      },
      "source": [
        "# Inputs for inference\n",
        "X_infer = pd.DataFrame([{'leukocyte_count': 13, 'blood_pressure': 12}])\n",
        "X_infer.head()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>leukocyte_count</th>\n",
              "      <th>blood_pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   leukocyte_count  blood_pressure\n",
              "0               13              12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qY6buxLKb2o",
        "colab_type": "code",
        "outputId": "8d5af37e-c89f-49a2-ff16-ddc6d31dab22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Standardize\n",
        "standardized_X_infer = X_scaler.transform(X_infer)\n",
        "print (standardized_X_infer)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.66523095 -3.08638693]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O5PbOAvXTzF",
        "colab_type": "code",
        "outputId": "a09f71c7-3548-4bb2-ffb8-888ccf705444",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Predict\n",
        "y_infer = model.predict(standardized_X_infer)\n",
        "_class = np.argmax(y_infer)\n",
        "print (f\"The probability that you have a {classes[_class]} tumor is {y_infer[0][_class]*100.0:.0f}%\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The probability that you have a benign tumor is 99%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PLPFFP67tvL",
        "colab_type": "text"
      },
      "source": [
        "# Unscaled weights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jv6LKNXO7uch",
        "colab_type": "text"
      },
      "source": [
        "Which of the features are most influential?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XYBW85WIdKy",
        "colab_type": "text"
      },
      "source": [
        "Note that only X was standardized.\n",
        "\n",
        "$\\hat{y}_{unscaled} = b_{scaled} + \\sum_{j=1}^{k}W_{{scaled}_j}x_{{scaled}_j}$\n",
        "* $x_{scaled} = \\frac{x_j - \\bar{x}_j}{\\sigma_j}$\n",
        "\n",
        "$\\hat{y}_{unscaled} = b_{scaled} + \\sum_{j=1}^{k} W_{{scaled}_j} (\\frac{x_j - \\bar{x}_j}{\\sigma_j}) $\n",
        "\n",
        "$\\hat{y}_{unscaled} = (b_{scaled} - \\sum_{j=1}^{k} W_{{scaled}_j}\\frac{\\bar{x}_j}{\\sigma_j}) +  \\sum_{j=1}^{k} (\\frac{W_{{scaled}_j}}{\\sigma_j})x_j$\n",
        "\n",
        "In the expression above, we can see the expression $\\hat{y}_{unscaled} = W_{unscaled}x + b_{unscaled} $\n",
        "\n",
        "* $W_{unscaled} = \\sum_{j=1}^{k} (\\frac{W_{{scaled}_j}}{\\sigma_j}) $\n",
        "\n",
        "* $b_{unscaled} = b_{scaled} - \\sum_{j=1}^{k} W_{{scaled}_j}\\frac{\\bar{x}_j}{\\sigma_j}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTSpxbwy7ugl",
        "colab_type": "code",
        "outputId": "cfd97bc5-af04-4ddd-8891-0e1aa29b14fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Unstandardize weights \n",
        "W = model.layers[0].get_weights()[0]\n",
        "b = model.layers[0].get_weights()[1]\n",
        "W_unscaled = W / X_scaler.scale_\n",
        "b_unscaled = b - np.sum((W_unscaled * X_scaler.mean_))\n",
        "print (W_unscaled)\n",
        "print (b_unscaled)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  3.89990762 -10.87473838]\n",
            " [ -1.58108892   3.92517096]]\n",
            "[68.22471 75.63666]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbcuwPdKEMlK",
        "colab_type": "text"
      },
      "source": [
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}